first commit

deploy/README.md

@@ -0,0 +1,30 @@
+English | [简体中文](README_ch.md)
+
+# PP-OCR Deployment
+
+- [PP-OCR Deployment](#pp-ocr-deployment)
+  - [Paddle Deployment Introduction](#paddle-deployment-introduction)
+  - [PP-OCR Deployment](#pp-ocr-deployment-1)
+
+<a name="1"></a>
+## Paddle Deployment Introduction
+
+Paddle provides a variety of deployment schemes to meet the deployment requirements of different scenarios. Please choose according to the actual situation:
+
+<div align="center">
+    <img src="../doc/deployment_en.png" width="800">
+</div>
+
+
+<a name="2"></a>
+## PP-OCR Deployment
+
+PP-OCR has supported multi deployment schemes. Click the link to get the specific tutorial.
+
+- [Python Inference](../doc/doc_en/inference_ppocr_en.md)
+- [C++ Inference](./cpp_infer/readme.md)
+- [Serving (Python/C++)](./pdserving/README.md)
+- [Paddle-Lite (ARM CPU/OpenCL ARM GPU)](./lite/readme.md)
+- [Paddle2ONNX](./paddle2onnx/readme.md)
+
+If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。

deploy/README_ch.md

@@ -0,0 +1,30 @@
+[English](README.md) | 简体中文
+
+# PP-OCR 模型推理部署
+
+- [Paddle 推理部署方式简介](#1)
+- [PP-OCR 推理部署](#2)
+
+<a name="1"></a>
+
+## Paddle 推理部署方式简介
+
+飞桨提供多种部署方案，以满足不同场景的部署需求，请根据实际情况进行选择：
+
+<div align="center">
+    <img src="../doc/deployment.png" width="800">
+</div>
+
+<a name="2"></a>
+
+## PP-OCR 推理部署
+
+PP-OCR模型已打通多种场景部署方案，点击链接获取具体的使用教程。
+
+- [Python 推理](../doc/doc_ch/inference_ppocr.md)
+- [C++ 推理](./cpp_infer/readme_ch.md)
+- [Serving 服务化部署（Python/C++）](./pdserving/README_CN.md)
+- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU）](./lite/readme_ch.md)
+- [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
+
+需要PP-OCR以外的学术算法模型的推理部署，请直接进入相应算法主页面，[入口](../doc/doc_ch/algorithm_overview.md)。

deploy/android_demo/.gitignore

@@ -0,0 +1,8 @@
+*.iml
+.gradle
+/local.properties
+/.idea/*
+.DS_Store
+/build
+/captures
+.externalNativeBuild

deploy/android_demo/README.md

@@ -0,0 +1,118 @@
+- [Android Demo](#android-demo)
+  - [1. 简介](#1-简介)
+  - [2. 近期更新](#2-近期更新)
+  - [3. 快速使用](#3-快速使用)
+    - [3.1 环境准备](#31-环境准备)
+    - [3.2 导入项目](#32-导入项目)
+    - [3.3 运行demo](#33-运行demo)
+    - [3.4 运行模式](#34-运行模式)
+    - [3.5 设置](#35-设置)
+  - [4 更多支持](#4-更多支持)
+
+# Android Demo
+
+## 1. 简介
+此为PaddleOCR的Android Demo，目前支持文本检测，文本方向分类器和文本识别模型的使用。使用 [PaddleLite v2.10](https://github.com/PaddlePaddle/Paddle-Lite/tree/release/v2.10) 进行开发。
+
+## 2. 近期更新
+* 2022.02.27
+    * 预测库更新到PaddleLite v2.10
+    * 支持6种运行模式：
+      * 检测+分类+识别
+      * 检测+识别
+      * 分类+识别
+      * 检测
+      * 识别
+      * 分类
+
+## 3. 快速使用
+
+### 3.1 环境准备
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Studio 官网](https://developer.android.com/studio)。
+2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
+
+**注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 Paddle Lite 预测库版本一样的 NDK
+
+### 3.2 导入项目
+
+点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
+导入完成后呈现如下界面
+![](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/import_demo.jpg)
+
+### 3.3 运行demo
+将手机连接上电脑后，点击Android Studio工具栏中的运行按钮即可运行demo。在此过程中，手机会弹出"允许从 USB 安装软件权限"的弹窗，点击允许即可。
+
+软件安转到手机上后会在手机主屏最后一页看到如下app
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/install_finish.jpeg" width="400">
+</div>
+
+点击app图标即可启动app，启动后app主页如下
+
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/main_page.jpg" width="400">
+</div>
+
+app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们的功能分别为
+
+* 运行模型：按照已选择的模式，运行对应的模型组合
+* 拍照识别：唤起手机相机拍照并获取拍照的图像，拍照完成后需要点击运行模型进行识别
+* 选取图片：唤起手机相册拍照选择图像，选择完成后需要点击运行模型进行识别
+* 清空绘图：清空当前显示图像上绘制的文本框，以便进行下一次识别(每次识别使用的图像都是当前显示的图像)
+* 下拉列表：进行运行模式的选择，目前包含6种运行模式，默认模式为**检测+分类+识别**详细说明见下一节。
+* 菜单按钮：点击后会进入菜单界面，进行模型和内置图像有关设置
+
+点击运行模型后，会按照所选择的模式运行对应的模型，**检测+分类+识别**模式下运行的模型结果如下所示：
+
+<img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400">
+
+模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model succeeded`表明模型运行成功。
+
+模型的运行结果显示在运行结果显示区，显示格式为
+```text
+序号：Det：(x1,y1)(x2,y2)(x3,y3)(x4,y4) Rec: 识别文本,识别置信度 Cls：分类类别,分类分时
+```
+
+### 3.4 运行模式
+
+PaddleOCR demo共提供了6种运行模式，如下图
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/select_mode.jpg" width="400">
+</div>
+
+每种模式的运行结果如下表所示
+
+| 检测+分类+识别                                                                                       | 检测+识别                                                                                      | 分类+识别                                                                                      |
+|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls_rec.jpg" width="400"> |
+
+
+| 检测                                                                                     | 识别                                                                                     | 分类                                                                                     |
+|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls.jpg" width="400"> |
+
+### 3.5 设置
+
+设置界面如下
+
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/settings.jpg" width="400">
+</div>
+
+在设置界面可以进行如下几项设定：
+1. 普通设置
+   * Enable custom settings: 选中状态下才能更改设置
+   * Model Path: 所运行的模型地址，使用默认值就好
+   * Label Path: 识别模型的字典
+   * Image Path: 进行识别的内置图像名
+2. 模型运行态设置，此项设置更改后返回主界面时，会自动重新加载模型
+   * CPU Thread Num: 模型运行使用的CPU核心数量
+   * CPU Power Mode: 模型运行模式，大小核设定
+3. 输入设置
+   * det long size: DB模型预处理时图像的长边长度，超过此长度resize到该值，短边进行等比例缩放，小于此长度不进行处理。
+4. 输出设置
+   * Score Threshold: DB模型后处理box的阈值，低于此阈值的box进行过滤，不显示。
+
+## 4 更多支持
+1. 实时识别，更新预测库可参考 https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/ocr/android/app/cxx/ppocr_demo
+2. 更多Paddle-Lite相关问题可前往[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) ，获得更多开发支持

deploy/android_demo/app/.gitignore

@@ -0,0 +1 @@
+/build

deploy/android_demo/app/build.gradle

@@ -0,0 +1,93 @@
+import java.security.MessageDigest
+
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 29
+    defaultConfig {
+        applicationId "com.baidu.paddle.lite.demo.ocr"
+        minSdkVersion 23
+        targetSdkVersion 29
+        versionCode 2
+        versionName "2.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+        externalNativeBuild {
+            cmake {
+                cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
+                arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
+            }
+        }
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path "src/main/cpp/CMakeLists.txt"
+            version "3.10.2"
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(include: ['*.jar'], dir: 'libs')
+    implementation 'androidx.appcompat:appcompat:1.1.0'
+    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+}
+
+def archives = [
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/libs/paddle_lite_libs_v2_10.tar.gz',
+                'dest': 'PaddleLite'
+        ],
+        [
+                'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
+                'dest': 'OpenCV'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2.tar.gz',
+                'dest' : 'src/main/assets/models'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_dict.tar.gz',
+                'dest' : 'src/main/assets/labels'
+        ]
+]
+
+task downloadAndExtractArchives(type: DefaultTask) {
+    doFirst {
+        println "Downloading and extracting archives including libs and models"
+    }
+    doLast {
+        // Prepare cache folder for archives
+        String cachePath = "cache"
+        if (!file("${cachePath}").exists()) {
+            mkdir "${cachePath}"
+        }
+        archives.eachWithIndex { archive, index ->
+            MessageDigest messageDigest = MessageDigest.getInstance('MD5')
+            messageDigest.update(archive.src.bytes)
+            String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
+            // Download the target archive if not exists
+            boolean copyFiles = !file("${archive.dest}").exists()
+            if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
+                ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
+                copyFiles = true; // force to copy files from the latest archive files
+            }
+            // Extract the target archive if its dest path does not exists
+            if (copyFiles) {
+                copy {
+                    from tarTree("${cachePath}/${cacheName}.tar.gz")
+                    into "${archive.dest}"
+                }
+            }
+        }
+    }
+}
+preBuild.dependsOn downloadAndExtractArchives

deploy/android_demo/app/proguard-rules.pro

@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java

@@ -0,0 +1,26 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.support.test.InstrumentationRegistry;
+import android.support.test.runner.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getTargetContext();
+
+        assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
+    }
+}

deploy/android_demo/app/src/main/AndroidManifest.xml

@@ -0,0 +1,38 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+          package="com.baidu.paddle.lite.demo.ocr">
+
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.CAMERA"/>
+
+
+    <application
+            android:allowBackup="true"
+            android:icon="@mipmap/ic_launcher"
+            android:label="@string/app_name"
+            android:roundIcon="@mipmap/ic_launcher_round"
+            android:supportsRtl="true"
+            android:theme="@style/AppTheme">
+        <activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN"/>
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+        <activity
+                android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
+                android:label="Settings">
+        </activity>
+        <provider
+            android:name="androidx.core.content.FileProvider"
+            android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
+            android:exported="false"
+            android:grantUriPermissions="true">
+            <meta-data
+                android:name="android.support.FILE_PROVIDER_PATHS"
+                android:resource="@xml/file_paths"></meta-data>
+        </provider>
+    </application>
+
+</manifest>

deploy/android_demo/app/src/main/cpp/CMakeLists.txt

@@ -0,0 +1,117 @@
+# For more information about using CMake with Android Studio, read the
+# documentation: https://d.android.com/studio/projects/add-native-code.html
+
+# Sets the minimum version of CMake required to build the native library.
+
+cmake_minimum_required(VERSION 3.4.1)
+
+# Creates and names a library, sets it as either STATIC or SHARED, and provides
+# the relative paths to its source code. You can define multiple libraries, and
+# CMake builds them for you. Gradle automatically packages shared libraries with
+# your APK.
+
+set(PaddleLite_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../PaddleLite")
+include_directories(${PaddleLite_DIR}/cxx/include)
+
+set(OpenCV_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../OpenCV/sdk/native/jni")
+message(STATUS "opencv dir: ${OpenCV_DIR}")
+find_package(OpenCV REQUIRED)
+message(STATUS "OpenCV libraries: ${OpenCV_LIBS}")
+include_directories(${OpenCV_INCLUDE_DIRS})
+aux_source_directory(. SOURCES)
+set(CMAKE_CXX_FLAGS
+        "${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os"
+        )
+set(CMAKE_CXX_FLAGS
+        "${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -fdata-sections -ffunction-sections"
+        )
+set(CMAKE_SHARED_LINKER_FLAGS
+        "${CMAKE_SHARED_LINKER_FLAGS} -Wl,--gc-sections -Wl,-z,nocopyreloc")
+
+add_library(
+        # Sets the name of the library.
+        Native
+        # Sets the library as a shared library.
+        SHARED
+        # Provides a relative path to your source file(s).
+        ${SOURCES})
+
+find_library(
+        # Sets the name of the path variable.
+        log-lib
+        # Specifies the name of the NDK library that you want CMake to locate.
+        log)
+
+add_library(
+        # Sets the name of the library.
+        paddle_light_api_shared
+        # Sets the library as a shared library.
+        SHARED
+        # Provides a relative path to your source file(s).
+        IMPORTED)
+
+set_target_properties(
+        # Specifies the target library.
+        paddle_light_api_shared
+        # Specifies the parameter you want to define.
+        PROPERTIES
+        IMPORTED_LOCATION
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
+        # Provides the path to the library you want to import.
+)
+
+
+# Specifies libraries CMake should link to your target library. You can link
+# multiple libraries, such as libraries you define in this build script,
+# prebuilt third-party libraries, or system libraries.
+
+target_link_libraries(
+        # Specifies the target library.
+        Native
+        paddle_light_api_shared
+        ${OpenCV_LIBS}
+        GLESv2
+        EGL
+        jnigraphics
+        ${log-lib}
+)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libc++_shared.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libc++_shared.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libpaddle_light_api_shared.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir_build.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir_build.so)

deploy/android_demo/app/src/main/cpp/common.h

@@ -0,0 +1,37 @@
+//
+// Created by fu on 4/25/18.
+//
+
+#pragma once
+#import <numeric>
+#import <vector>
+
+#ifdef __ANDROID__
+
+#include <android/log.h>
+
+#define LOG_TAG "OCR_NDK"
+
+#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
+#define LOGW(...) __android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__)
+#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)
+#else
+#include <stdio.h>
+#define LOGI(format, ...)                                                      \
+  fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
+#define LOGW(format, ...)                                                      \
+  fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
+#define LOGE(format, ...)                                                      \
+  fprintf(stderr, "[" LOG_TAG "]Error: " format "\n", ##__VA_ARGS__)
+#endif
+
+enum RETURN_CODE { RETURN_OK = 0 };
+
+enum NET_TYPE { NET_OCR = 900100, NET_OCR_INTERNAL = 991008 };
+
+template <typename T> inline T product(const std::vector<T> &vec) {
+  if (vec.empty()) {
+    return 0;
+  }
+  return std::accumulate(vec.begin(), vec.end(), 1, std::multiplies<T>());
+}

deploy/android_demo/app/src/main/cpp/native.cpp

@@ -0,0 +1,120 @@
+//
+// Created by fujiayi on 2020/7/5.
+//
+
+#include "native.h"
+#include "ocr_ppredictor.h"
+#include <algorithm>
+#include <paddle_api.h>
+#include <string>
+
+static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
+
+extern "C" JNIEXPORT jlong JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
+    JNIEnv *env, jobject thiz, jstring j_det_model_path,
+    jstring j_rec_model_path, jstring j_cls_model_path, jint j_use_opencl,
+    jint j_thread_num, jstring j_cpu_mode) {
+  std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
+  std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
+  std::string cls_model_path = jstring_to_cpp_string(env, j_cls_model_path);
+  int thread_num = j_thread_num;
+  std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
+  ppredictor::OCR_Config conf;
+  conf.use_opencl = j_use_opencl;
+  conf.thread_num = thread_num;
+  conf.mode = str_to_cpu_mode(cpu_mode);
+  ppredictor::OCR_PPredictor *orc_predictor =
+      new ppredictor::OCR_PPredictor{conf};
+  orc_predictor->init_from_file(det_model_path, rec_model_path, cls_model_path);
+  return reinterpret_cast<jlong>(orc_predictor);
+}
+
+/**
+ * "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
+ * @param cpu_mode
+ * @return
+ */
+static paddle::lite_api::PowerMode
+str_to_cpu_mode(const std::string &cpu_mode) {
+  static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
+      {"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
+      {"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
+      {"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
+      {"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}};
+  std::string upper_key;
+  std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
+                 ::toupper);
+  auto index = cpu_mode_map.find(upper_key.c_str());
+  if (index == cpu_mode_map.end()) {
+    LOGE("cpu_mode not found %s", upper_key.c_str());
+    return paddle::lite_api::LITE_POWER_HIGH;
+  } else {
+    return index->second;
+  }
+}
+
+extern "C" JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
+    JNIEnv *env, jobject thiz, jlong java_pointer, jobject original_image,
+    jint j_max_size_len, jint j_run_det, jint j_run_cls, jint j_run_rec) {
+  LOGI("begin to run native forward");
+  if (java_pointer == 0) {
+    LOGE("JAVA pointer is NULL");
+    return cpp_array_to_jfloatarray(env, nullptr, 0);
+  }
+
+  cv::Mat origin = bitmap_to_cv_mat(env, original_image);
+  if (origin.size == 0) {
+    LOGE("origin bitmap cannot convert to CV Mat");
+    return cpp_array_to_jfloatarray(env, nullptr, 0);
+  }
+
+  int max_size_len = j_max_size_len;
+  int run_det = j_run_det;
+  int run_cls = j_run_cls;
+  int run_rec = j_run_rec;
+
+  ppredictor::OCR_PPredictor *ppredictor =
+      (ppredictor::OCR_PPredictor *)java_pointer;
+  std::vector<int64_t> dims_arr;
+  std::vector<ppredictor::OCRPredictResult> results =
+      ppredictor->infer_ocr(origin, max_size_len, run_det, run_cls, run_rec);
+  LOGI("infer_ocr finished with boxes %ld", results.size());
+
+  // 这里将std::vector<ppredictor::OCRPredictResult> 序列化成
+  // float数组，传输到java层再反序列化
+  std::vector<float> float_arr;
+  for (const ppredictor::OCRPredictResult &r : results) {
+    float_arr.push_back(r.points.size());
+    float_arr.push_back(r.word_index.size());
+    float_arr.push_back(r.score);
+    // add det point
+    for (const std::vector<int> &point : r.points) {
+      float_arr.push_back(point.at(0));
+      float_arr.push_back(point.at(1));
+    }
+    // add rec word idx
+    for (int index : r.word_index) {
+      float_arr.push_back(index);
+    }
+    // add cls result
+    float_arr.push_back(r.cls_label);
+    float_arr.push_back(r.cls_score);
+  }
+  return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
+}
+
+extern "C" JNIEXPORT void JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(
+    JNIEnv *env, jobject thiz, jlong java_pointer) {
+  if (java_pointer == 0) {
+    LOGE("JAVA pointer is NULL");
+    return;
+  }
+  ppredictor::OCR_PPredictor *ppredictor =
+      (ppredictor::OCR_PPredictor *)java_pointer;
+  delete ppredictor;
+}

deploy/android_demo/app/src/main/cpp/native.h

@@ -0,0 +1,137 @@
+//
+// Created by fujiayi on 2020/7/5.
+//
+
+#pragma once
+
+#include "common.h"
+#include <android/bitmap.h>
+#include <jni.h>
+#include <opencv2/opencv.hpp>
+#include <string>
+#include <vector>
+
+inline std::string jstring_to_cpp_string(JNIEnv *env, jstring jstr) {
+  // In java, a unicode char will be encoded using 2 bytes (utf16).
+  // so jstring will contain characters utf16. std::string in c++ is
+  // essentially a string of bytes, not characters, so if we want to
+  // pass jstring from JNI to c++, we have convert utf16 to bytes.
+  if (!jstr) {
+    return "";
+  }
+  const jclass stringClass = env->GetObjectClass(jstr);
+  const jmethodID getBytes =
+      env->GetMethodID(stringClass, "getBytes", "(Ljava/lang/String;)[B");
+  const jbyteArray stringJbytes = (jbyteArray)env->CallObjectMethod(
+      jstr, getBytes, env->NewStringUTF("UTF-8"));
+
+  size_t length = (size_t)env->GetArrayLength(stringJbytes);
+  jbyte *pBytes = env->GetByteArrayElements(stringJbytes, NULL);
+
+  std::string ret = std::string(reinterpret_cast<char *>(pBytes), length);
+  env->ReleaseByteArrayElements(stringJbytes, pBytes, JNI_ABORT);
+
+  env->DeleteLocalRef(stringJbytes);
+  env->DeleteLocalRef(stringClass);
+  return ret;
+}
+
+inline jstring cpp_string_to_jstring(JNIEnv *env, std::string str) {
+  auto *data = str.c_str();
+  jclass strClass = env->FindClass("java/lang/String");
+  jmethodID strClassInitMethodID =
+      env->GetMethodID(strClass, "<init>", "([BLjava/lang/String;)V");
+
+  jbyteArray bytes = env->NewByteArray(strlen(data));
+  env->SetByteArrayRegion(bytes, 0, strlen(data),
+                          reinterpret_cast<const jbyte *>(data));
+
+  jstring encoding = env->NewStringUTF("UTF-8");
+  jstring res = (jstring)(env->NewObject(strClass, strClassInitMethodID, bytes,
+                                         encoding));
+
+  env->DeleteLocalRef(strClass);
+  env->DeleteLocalRef(encoding);
+  env->DeleteLocalRef(bytes);
+
+  return res;
+}
+
+inline jfloatArray cpp_array_to_jfloatarray(JNIEnv *env, const float *buf,
+                                            int64_t len) {
+  if (len == 0) {
+    return env->NewFloatArray(0);
+  }
+  jfloatArray result = env->NewFloatArray(len);
+  env->SetFloatArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jintArray cpp_array_to_jintarray(JNIEnv *env, const int *buf,
+                                        int64_t len) {
+  jintArray result = env->NewIntArray(len);
+  env->SetIntArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jbyteArray cpp_array_to_jbytearray(JNIEnv *env, const int8_t *buf,
+                                          int64_t len) {
+  jbyteArray result = env->NewByteArray(len);
+  env->SetByteArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jlongArray int64_vector_to_jlongarray(JNIEnv *env,
+                                             const std::vector<int64_t> &vec) {
+  jlongArray result = env->NewLongArray(vec.size());
+  jlong *buf = new jlong[vec.size()];
+  for (size_t i = 0; i < vec.size(); ++i) {
+    buf[i] = (jlong)vec[i];
+  }
+  env->SetLongArrayRegion(result, 0, vec.size(), buf);
+  delete[] buf;
+  return result;
+}
+
+inline std::vector<int64_t> jlongarray_to_int64_vector(JNIEnv *env,
+                                                       jlongArray data) {
+  int data_size = env->GetArrayLength(data);
+  jlong *data_ptr = env->GetLongArrayElements(data, nullptr);
+  std::vector<int64_t> data_vec(data_ptr, data_ptr + data_size);
+  env->ReleaseLongArrayElements(data, data_ptr, 0);
+  return data_vec;
+}
+
+inline std::vector<float> jfloatarray_to_float_vector(JNIEnv *env,
+                                                      jfloatArray data) {
+  int data_size = env->GetArrayLength(data);
+  jfloat *data_ptr = env->GetFloatArrayElements(data, nullptr);
+  std::vector<float> data_vec(data_ptr, data_ptr + data_size);
+  env->ReleaseFloatArrayElements(data, data_ptr, 0);
+  return data_vec;
+}
+
+inline cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
+  AndroidBitmapInfo info;
+  int result = AndroidBitmap_getInfo(env, bitmap, &info);
+  if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
+    LOGE("AndroidBitmap_getInfo failed, result: %d", result);
+    return cv::Mat{};
+  }
+  if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
+    LOGE("Bitmap format is not RGBA_8888 !");
+    return cv::Mat{};
+  }
+  unsigned char *srcData = NULL;
+  AndroidBitmap_lockPixels(env, bitmap, (void **)&srcData);
+  cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
+  memcpy(mat.data, srcData, info.height * info.width * 4);
+  AndroidBitmap_unlockPixels(env, bitmap);
+  cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
+  /**
+  if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
+      LOGE("Write image failed " );
+  }
+   */
+  return mat;
+}

deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp

@@ -0,0 +1,540 @@
+/*******************************************************************************
+ *                                                                              *
+ * Author    :  Angus Johnson * Version   :  6.4.2 * Date      :  27 February
+ *2017                                                * Website   :
+ *http://www.angusj.com                                           * Copyright :
+ *Angus Johnson 2010-2017                                         *
+ *                                                                              *
+ * License: * Use, modification & distribution is subject to Boost Software
+ *License Ver 1. * http://www.boost.org/LICENSE_1_0.txt *
+ *                                                                              *
+ * Attributions: * The code in this library is an extension of Bala Vatti's
+ *clipping algorithm: * "A generic solution to polygon clipping" *
+ * Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63. *
+ * http://portal.acm.org/citation.cfm?id=129906 *
+ *                                                                              *
+ * Computer graphics and geometric modeling: implementation and algorithms * By
+ *Max K. Agoston                                                            *
+ * Springer; 1 edition (January 4, 2005) *
+ * http://books.google.com/books?q=vatti+clipping+agoston *
+ *                                                                              *
+ * See also: * "Polygon Offsetting by Computing Winding Numbers" * Paper no.
+ *DETC2005-85513 pp. 565-575                                         * ASME 2005
+ *International Design Engineering Technical Conferences             * and
+ *Computers and Information in Engineering Conference (IDETC/CIE2005)      *
+ * September 24-28, 2005 , Long Beach, California, USA *
+ * http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf *
+ *                                                                              *
+ *******************************************************************************/
+
+#ifndef clipper_hpp
+#define clipper_hpp
+
+#define CLIPPER_VERSION "6.4.2"
+
+// use_int32: When enabled 32bit ints are used instead of 64bit ints. This
+// improve performance but coordinate values are limited to the range +/- 46340
+//#define use_int32
+
+// use_xyz: adds a Z member to IntPoint. Adds a minor cost to performance.
+//#define use_xyz
+
+// use_lines: Enables line clipping. Adds a very minor cost to performance.
+#define use_lines
+
+// use_deprecated: Enables temporary support for the obsolete functions
+//#define use_deprecated
+
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <list>
+#include <ostream>
+#include <queue>
+#include <set>
+#include <stdexcept>
+#include <vector>
+
+namespace ClipperLib {
+
+enum ClipType { ctIntersection, ctUnion, ctDifference, ctXor };
+enum PolyType { ptSubject, ptClip };
+// By far the most widely used winding rules for polygon filling are
+// EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
+// Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
+// see http://glprogramming.com/red/chapter11.html
+enum PolyFillType { pftEvenOdd, pftNonZero, pftPositive, pftNegative };
+
+#ifdef use_int32
+typedef int cInt;
+static cInt const loRange = 0x7FFF;
+static cInt const hiRange = 0x7FFF;
+#else
+typedef signed long long cInt;
+static cInt const loRange = 0x3FFFFFFF;
+static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
+typedef signed long long long64; // used by Int128 class
+typedef unsigned long long ulong64;
+
+#endif
+
+struct IntPoint {
+  cInt X;
+  cInt Y;
+#ifdef use_xyz
+  cInt Z;
+  IntPoint(cInt x = 0, cInt y = 0, cInt z = 0) : X(x), Y(y), Z(z){};
+#else
+
+  IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y){};
+#endif
+
+  friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
+    return a.X == b.X && a.Y == b.Y;
+  }
+
+  friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
+    return a.X != b.X || a.Y != b.Y;
+  }
+};
+//------------------------------------------------------------------------------
+
+typedef std::vector<IntPoint> Path;
+typedef std::vector<Path> Paths;
+
+inline Path &operator<<(Path &poly, const IntPoint &p) {
+  poly.push_back(p);
+  return poly;
+}
+
+inline Paths &operator<<(Paths &polys, const Path &p) {
+  polys.push_back(p);
+  return polys;
+}
+
+std::ostream &operator<<(std::ostream &s, const IntPoint &p);
+
+std::ostream &operator<<(std::ostream &s, const Path &p);
+
+std::ostream &operator<<(std::ostream &s, const Paths &p);
+
+struct DoublePoint {
+  double X;
+  double Y;
+
+  DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
+
+  DoublePoint(IntPoint ip) : X((double)ip.X), Y((double)ip.Y) {}
+};
+//------------------------------------------------------------------------------
+
+#ifdef use_xyz
+typedef void (*ZFillCallback)(IntPoint &e1bot, IntPoint &e1top, IntPoint &e2bot,
+                              IntPoint &e2top, IntPoint &pt);
+#endif
+
+enum InitOptions {
+  ioReverseSolution = 1,
+  ioStrictlySimple = 2,
+  ioPreserveCollinear = 4
+};
+enum JoinType { jtSquare, jtRound, jtMiter };
+enum EndType {
+  etClosedPolygon,
+  etClosedLine,
+  etOpenButt,
+  etOpenSquare,
+  etOpenRound
+};
+
+class PolyNode;
+
+typedef std::vector<PolyNode *> PolyNodes;
+
+class PolyNode {
+public:
+  PolyNode();
+
+  virtual ~PolyNode(){};
+  Path Contour;
+  PolyNodes Children;
+  PolyNode *Parent;
+
+  PolyNode *GetNext() const;
+
+  bool IsHole() const;
+
+  bool IsOpen() const;
+
+  int ChildCount() const;
+
+private:
+  // PolyNode& operator =(PolyNode& other);
+  unsigned Index; // node index in Parent.Children
+  bool m_IsOpen;
+  JoinType m_jointype;
+  EndType m_endtype;
+
+  PolyNode *GetNextSiblingUp() const;
+
+  void AddChild(PolyNode &child);
+
+  friend class Clipper; // to access Index
+  friend class ClipperOffset;
+};
+
+class PolyTree : public PolyNode {
+public:
+  ~PolyTree() { Clear(); };
+
+  PolyNode *GetFirst() const;
+
+  void Clear();
+
+  int Total() const;
+
+private:
+  // PolyTree& operator =(PolyTree& other);
+  PolyNodes AllNodes;
+
+  friend class Clipper; // to access AllNodes
+};
+
+bool Orientation(const Path &poly);
+
+double Area(const Path &poly);
+
+int PointInPolygon(const IntPoint &pt, const Path &path);
+
+void SimplifyPolygon(const Path &in_poly, Paths &out_polys,
+                     PolyFillType fillType = pftEvenOdd);
+
+void SimplifyPolygons(const Paths &in_polys, Paths &out_polys,
+                      PolyFillType fillType = pftEvenOdd);
+
+void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
+
+void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
+
+void CleanPolygon(Path &poly, double distance = 1.415);
+
+void CleanPolygons(const Paths &in_polys, Paths &out_polys,
+                   double distance = 1.415);
+
+void CleanPolygons(Paths &polys, double distance = 1.415);
+
+void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution,
+                  bool pathIsClosed);
+
+void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution,
+                  bool pathIsClosed);
+
+void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
+
+void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
+
+void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
+
+void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
+
+void ReversePath(Path &p);
+
+void ReversePaths(Paths &p);
+
+struct IntRect {
+  cInt left;
+  cInt top;
+  cInt right;
+  cInt bottom;
+};
+
+// enums that are used internally ...
+enum EdgeSide { esLeft = 1, esRight = 2 };
+
+// forward declarations (for stuff used internally) ...
+struct TEdge;
+struct IntersectNode;
+struct LocalMinimum;
+struct OutPt;
+struct OutRec;
+struct Join;
+
+typedef std::vector<OutRec *> PolyOutList;
+typedef std::vector<TEdge *> EdgeList;
+typedef std::vector<Join *> JoinList;
+typedef std::vector<IntersectNode *> IntersectList;
+
+//------------------------------------------------------------------------------
+
+// ClipperBase is the ancestor to the Clipper class. It should not be
+// instantiated directly. This class simply abstracts the conversion of sets of
+// polygon coordinates into edge objects that are stored in a LocalMinima list.
+class ClipperBase {
+public:
+  ClipperBase();
+
+  virtual ~ClipperBase();
+
+  virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
+
+  bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
+
+  virtual void Clear();
+
+  IntRect GetBounds();
+
+  bool PreserveCollinear() { return m_PreserveCollinear; };
+
+  void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
+
+protected:
+  void DisposeLocalMinimaList();
+
+  TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
+
+  virtual void Reset();
+
+  TEdge *ProcessBound(TEdge *E, bool IsClockwise);
+
+  void InsertScanbeam(const cInt Y);
+
+  bool PopScanbeam(cInt &Y);
+
+  bool LocalMinimaPending();
+
+  bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
+
+  OutRec *CreateOutRec();
+
+  void DisposeAllOutRecs();
+
+  void DisposeOutRec(PolyOutList::size_type index);
+
+  void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
+
+  void DeleteFromAEL(TEdge *e);
+
+  void UpdateEdgeIntoAEL(TEdge *&e);
+
+  typedef std::vector<LocalMinimum> MinimaList;
+  MinimaList::iterator m_CurrentLM;
+  MinimaList m_MinimaList;
+
+  bool m_UseFullRange;
+  EdgeList m_edges;
+  bool m_PreserveCollinear;
+  bool m_HasOpenPaths;
+  PolyOutList m_PolyOuts;
+  TEdge *m_ActiveEdges;
+
+  typedef std::priority_queue<cInt> ScanbeamList;
+  ScanbeamList m_Scanbeam;
+};
+//------------------------------------------------------------------------------
+
+class Clipper : public virtual ClipperBase {
+public:
+  Clipper(int initOptions = 0);
+
+  bool Execute(ClipType clipType, Paths &solution,
+               PolyFillType fillType = pftEvenOdd);
+
+  bool Execute(ClipType clipType, Paths &solution, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+
+  bool Execute(ClipType clipType, PolyTree &polytree,
+               PolyFillType fillType = pftEvenOdd);
+
+  bool Execute(ClipType clipType, PolyTree &polytree, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+
+  bool ReverseSolution() { return m_ReverseOutput; };
+
+  void ReverseSolution(bool value) { m_ReverseOutput = value; };
+
+  bool StrictlySimple() { return m_StrictSimple; };
+
+  void StrictlySimple(bool value) { m_StrictSimple = value; };
+// set the callback function for z value filling on intersections (otherwise Z
+// is 0)
+#ifdef use_xyz
+  void ZFillFunction(ZFillCallback zFillFunc);
+#endif
+protected:
+  virtual bool ExecuteInternal();
+
+private:
+  JoinList m_Joins;
+  JoinList m_GhostJoins;
+  IntersectList m_IntersectList;
+  ClipType m_ClipType;
+  typedef std::list<cInt> MaximaList;
+  MaximaList m_Maxima;
+  TEdge *m_SortedEdges;
+  bool m_ExecuteLocked;
+  PolyFillType m_ClipFillType;
+  PolyFillType m_SubjFillType;
+  bool m_ReverseOutput;
+  bool m_UsingPolyTree;
+  bool m_StrictSimple;
+#ifdef use_xyz
+  ZFillCallback m_ZFill; // custom callback
+#endif
+
+  void SetWindingCount(TEdge &edge);
+
+  bool IsEvenOddFillType(const TEdge &edge) const;
+
+  bool IsEvenOddAltFillType(const TEdge &edge) const;
+
+  void InsertLocalMinimaIntoAEL(const cInt botY);
+
+  void InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge);
+
+  void AddEdgeToSEL(TEdge *edge);
+
+  bool PopEdgeFromSEL(TEdge *&edge);
+
+  void CopyAELToSEL();
+
+  void DeleteFromSEL(TEdge *e);
+
+  void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
+
+  bool IsContributing(const TEdge &edge) const;
+
+  bool IsTopHorz(const cInt XPos);
+
+  void DoMaxima(TEdge *e);
+
+  void ProcessHorizontals();
+
+  void ProcessHorizontal(TEdge *horzEdge);
+
+  void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+
+  OutPt *AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+
+  OutRec *GetOutRec(int idx);
+
+  void AppendPolygon(TEdge *e1, TEdge *e2);
+
+  void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
+
+  OutPt *AddOutPt(TEdge *e, const IntPoint &pt);
+
+  OutPt *GetLastOutPt(TEdge *e);
+
+  bool ProcessIntersections(const cInt topY);
+
+  void BuildIntersectList(const cInt topY);
+
+  void ProcessIntersectList();
+
+  void ProcessEdgesAtTopOfScanbeam(const cInt topY);
+
+  void BuildResult(Paths &polys);
+
+  void BuildResult2(PolyTree &polytree);
+
+  void SetHoleState(TEdge *e, OutRec *outrec);
+
+  void DisposeIntersectNodes();
+
+  bool FixupIntersectionOrder();
+
+  void FixupOutPolygon(OutRec &outrec);
+
+  void FixupOutPolyline(OutRec &outrec);
+
+  bool IsHole(TEdge *e);
+
+  bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
+
+  void FixHoleLinkage(OutRec &outrec);
+
+  void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
+
+  void ClearJoins();
+
+  void ClearGhostJoins();
+
+  void AddGhostJoin(OutPt *op, const IntPoint offPt);
+
+  bool JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2);
+
+  void JoinCommonEdges();
+
+  void DoSimplePolygons();
+
+  void FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec);
+
+  void FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec);
+
+  void FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec);
+
+#ifdef use_xyz
+  void SetZ(IntPoint &pt, TEdge &e1, TEdge &e2);
+#endif
+};
+//------------------------------------------------------------------------------
+
+class ClipperOffset {
+public:
+  ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
+
+  ~ClipperOffset();
+
+  void AddPath(const Path &path, JoinType joinType, EndType endType);
+
+  void AddPaths(const Paths &paths, JoinType joinType, EndType endType);
+
+  void Execute(Paths &solution, double delta);
+
+  void Execute(PolyTree &solution, double delta);
+
+  void Clear();
+
+  double MiterLimit;
+  double ArcTolerance;
+
+private:
+  Paths m_destPolys;
+  Path m_srcPoly;
+  Path m_destPoly;
+  std::vector<DoublePoint> m_normals;
+  double m_delta, m_sinA, m_sin, m_cos;
+  double m_miterLim, m_StepsPerRad;
+  IntPoint m_lowest;
+  PolyNode m_polyNodes;
+
+  void FixOrientations();
+
+  void DoOffset(double delta);
+
+  void OffsetPoint(int j, int &k, JoinType jointype);
+
+  void DoSquare(int j, int k);
+
+  void DoMiter(int j, int k, double r);
+
+  void DoRound(int j, int k);
+};
+//------------------------------------------------------------------------------
+
+class clipperException : public std::exception {
+public:
+  clipperException(const char *description) : m_descr(description) {}
+
+  virtual ~clipperException() throw() {}
+
+  virtual const char *what() const throw() { return m_descr.c_str(); }
+
+private:
+  std::string m_descr;
+};
+//------------------------------------------------------------------------------
+
+} // namespace ClipperLib
+
+#endif // clipper_hpp

deploy/android_demo/app/src/main/cpp/ocr_cls_process.cpp

@@ -0,0 +1,45 @@
+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_cls_process.h"
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+const std::vector<int> CLS_IMAGE_SHAPE = {3, 48, 192};
+
+cv::Mat cls_resize_img(const cv::Mat &img) {
+  int imgC = CLS_IMAGE_SHAPE[0];
+  int imgW = CLS_IMAGE_SHAPE[2];
+  int imgH = CLS_IMAGE_SHAPE[1];
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_CUBIC);
+
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_w),
+                       cv::BORDER_CONSTANT, {0, 0, 0});
+  }
+  return resize_img;
+}

deploy/android_demo/app/src/main/cpp/ocr_cls_process.h

@@ -0,0 +1,23 @@
+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "common.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+extern const std::vector<int> CLS_IMAGE_SHAPE;
+
+cv::Mat cls_resize_img(const cv::Mat &img);

deploy/android_demo/app/src/main/cpp/ocr_crnn_process.cpp

@@ -0,0 +1,141 @@
+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_crnn_process.h"
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+const std::string CHARACTER_TYPE = "ch";
+const int MAX_DICT_LENGTH = 6624;
+const std::vector<int> REC_IMAGE_SHAPE = {3, 32, 320};
+
+static cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio) {
+  int imgC = REC_IMAGE_SHAPE[0];
+  int imgW = REC_IMAGE_SHAPE[2];
+  int imgH = REC_IMAGE_SHAPE[1];
+
+  if (CHARACTER_TYPE == "ch")
+    imgW = int(32 * wh_ratio);
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_CUBIC);
+
+  resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+  for (int h = 0; h < resize_img.rows; h++) {
+    for (int w = 0; w < resize_img.cols; w++) {
+      resize_img.at<cv::Vec3f>(h, w)[0] =
+          (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[1] =
+          (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[2] =
+          (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) * 2;
+    }
+  }
+
+  cv::Mat dist;
+  cv::copyMakeBorder(resize_img, dist, 0, 0, 0, int(imgW - resize_w),
+                     cv::BORDER_CONSTANT, {0, 0, 0});
+
+  return dist;
+}
+
+cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio) {
+  int imgC = REC_IMAGE_SHAPE[0];
+  int imgW = REC_IMAGE_SHAPE[2];
+  int imgH = REC_IMAGE_SHAPE[1];
+
+  if (CHARACTER_TYPE == "ch") {
+    imgW = int(32 * wh_ratio);
+  }
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH));
+  return resize_img;
+}
+
+cv::Mat get_rotate_crop_image(const cv::Mat &srcimage,
+                              const std::vector<std::vector<int>> &box) {
+
+  std::vector<std::vector<int>> points = box;
+
+  int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
+  int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
+  int left = int(*std::min_element(x_collect, x_collect + 4));
+  int right = int(*std::max_element(x_collect, x_collect + 4));
+  int top = int(*std::min_element(y_collect, y_collect + 4));
+  int bottom = int(*std::max_element(y_collect, y_collect + 4));
+
+  cv::Mat img_crop;
+  srcimage(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
+
+  for (int i = 0; i < points.size(); i++) {
+    points[i][0] -= left;
+    points[i][1] -= top;
+  }
+
+  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
+                                pow(points[0][1] - points[1][1], 2)));
+  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
+                                 pow(points[0][1] - points[3][1], 2)));
+
+  cv::Point2f pts_std[4];
+  pts_std[0] = cv::Point2f(0., 0.);
+  pts_std[1] = cv::Point2f(img_crop_width, 0.);
+  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
+  pts_std[3] = cv::Point2f(0.f, img_crop_height);
+
+  cv::Point2f pointsf[4];
+  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
+  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
+  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
+  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
+
+  cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
+
+  cv::Mat dst_img;
+  cv::warpPerspective(img_crop, dst_img, M,
+                      cv::Size(img_crop_width, img_crop_height),
+                      cv::BORDER_REPLICATE);
+
+  if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
+    /*
+    cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
+    cv::transpose(dst_img, srcCopy);
+    cv::flip(srcCopy, srcCopy, 0);
+    return srcCopy;
+    */
+    cv::transpose(dst_img, dst_img);
+    cv::flip(dst_img, dst_img, 0);
+    return dst_img;
+  } else {
+    return dst_img;
+  }
+}

deploy/android_demo/app/src/main/cpp/ocr_crnn_process.h

@@ -0,0 +1,20 @@
+//
+// Created by fujiayi on 2020/7/3.
+//
+#pragma once
+
+#include "common.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+extern const std::vector<int> REC_IMAGE_SHAPE;
+
+cv::Mat get_rotate_crop_image(const cv::Mat &srcimage,
+                              const std::vector<std::vector<int>> &box);
+
+cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio);
+
+template <class ForwardIterator>
+inline size_t argmax(ForwardIterator first, ForwardIterator last) {
+  return std::distance(first, std::max_element(first, last));
+}

deploy/android_demo/app/src/main/cpp/ocr_db_post_process.cpp

@@ -0,0 +1,342 @@
+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_clipper.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <iostream>
+#include <math.h>
+#include <vector>
+
+static void getcontourarea(float **box, float unclip_ratio, float &distance) {
+  int pts_num = 4;
+  float area = 0.0f;
+  float dist = 0.0f;
+  for (int i = 0; i < pts_num; i++) {
+    area += box[i][0] * box[(i + 1) % pts_num][1] -
+            box[i][1] * box[(i + 1) % pts_num][0];
+    dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
+                      (box[i][0] - box[(i + 1) % pts_num][0]) +
+                  (box[i][1] - box[(i + 1) % pts_num][1]) *
+                      (box[i][1] - box[(i + 1) % pts_num][1]));
+  }
+  area = fabs(float(area / 2.0));
+
+  distance = area * unclip_ratio / dist;
+}
+
+static cv::RotatedRect unclip(float **box) {
+  float unclip_ratio = 2.0;
+  float distance = 1.0;
+
+  getcontourarea(box, unclip_ratio, distance);
+
+  ClipperLib::ClipperOffset offset;
+  ClipperLib::Path p;
+  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
+    << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
+    << ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
+    << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
+  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
+
+  ClipperLib::Paths soln;
+  offset.Execute(soln, distance);
+  std::vector<cv::Point2f> points;
+
+  for (int j = 0; j < soln.size(); j++) {
+    for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
+      points.emplace_back(soln[j][i].X, soln[j][i].Y);
+    }
+  }
+  cv::RotatedRect res = cv::minAreaRect(points);
+
+  return res;
+}
+
+static float **Mat2Vec(cv::Mat mat) {
+  auto **array = new float *[mat.rows];
+  for (int i = 0; i < mat.rows; ++i) {
+    array[i] = new float[mat.cols];
+  }
+  for (int i = 0; i < mat.rows; ++i) {
+    for (int j = 0; j < mat.cols; ++j) {
+      array[i][j] = mat.at<float>(i, j);
+    }
+  }
+
+  return array;
+}
+
+static void quickSort(float **s, int l, int r) {
+  if (l < r) {
+    int i = l, j = r;
+    float x = s[l][0];
+    float *xp = s[l];
+    while (i < j) {
+      while (i < j && s[j][0] >= x) {
+        j--;
+      }
+      if (i < j) {
+        std::swap(s[i++], s[j]);
+      }
+      while (i < j && s[i][0] < x) {
+        i++;
+      }
+      if (i < j) {
+        std::swap(s[j--], s[i]);
+      }
+    }
+    s[i] = xp;
+    quickSort(s, l, i - 1);
+    quickSort(s, i + 1, r);
+  }
+}
+
+static void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r,
+                             int axis) {
+  if (l < r) {
+    int i = l, j = r;
+    int x = box[l][axis];
+    std::vector<int> xp(box[l]);
+    while (i < j) {
+      while (i < j && box[j][axis] >= x) {
+        j--;
+      }
+      if (i < j) {
+        std::swap(box[i++], box[j]);
+      }
+      while (i < j && box[i][axis] < x) {
+        i++;
+      }
+      if (i < j) {
+        std::swap(box[j--], box[i]);
+      }
+    }
+    box[i] = xp;
+    quickSort_vector(box, l, i - 1, axis);
+    quickSort_vector(box, i + 1, r, axis);
+  }
+}
+
+static std::vector<std::vector<int>>
+order_points_clockwise(std::vector<std::vector<int>> pts) {
+  std::vector<std::vector<int>> box = pts;
+  quickSort_vector(box, 0, int(box.size() - 1), 0);
+  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
+  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
+
+  if (leftmost[0][1] > leftmost[1][1]) {
+    std::swap(leftmost[0], leftmost[1]);
+  }
+
+  if (rightmost[0][1] > rightmost[1][1]) {
+    std::swap(rightmost[0], rightmost[1]);
+  }
+
+  std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1],
+                                        leftmost[1]};
+  return rect;
+}
+
+static float **get_mini_boxes(cv::RotatedRect box, float &ssid) {
+  ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
+
+  cv::Mat points;
+  cv::boxPoints(box, points);
+  // sorted box points
+  auto array = Mat2Vec(points);
+  quickSort(array, 0, 3);
+
+  float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
+  if (array[3][1] <= array[2][1]) {
+    idx2 = array[3];
+    idx3 = array[2];
+  } else {
+    idx2 = array[2];
+    idx3 = array[3];
+  }
+  if (array[1][1] <= array[0][1]) {
+    idx1 = array[1];
+    idx4 = array[0];
+  } else {
+    idx1 = array[0];
+    idx4 = array[1];
+  }
+
+  array[0] = idx1;
+  array[1] = idx2;
+  array[2] = idx3;
+  array[3] = idx4;
+
+  return array;
+}
+
+template <class T> T clamp(T x, T min, T max) {
+  if (x > max) {
+    return max;
+  }
+  if (x < min) {
+    return min;
+  }
+  return x;
+}
+
+static float clampf(float x, float min, float max) {
+  if (x > max)
+    return max;
+  if (x < min)
+    return min;
+  return x;
+}
+
+float box_score_fast(float **box_array, cv::Mat pred) {
+  auto array = box_array;
+  int width = pred.cols;
+  int height = pred.rows;
+
+  float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
+  float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
+
+  int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+  int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point root_point[4];
+  root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
+  root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
+  root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
+  root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
+  const cv::Point *ppt[1] = {root_point};
+  int npt[] = {4};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+
+  auto score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
+std::vector<std::vector<std::vector<int>>>
+boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap) {
+  const int min_size = 3;
+  const int max_candidates = 1000;
+  const float box_thresh = 0.5;
+
+  int width = bitmap.cols;
+  int height = bitmap.rows;
+
+  std::vector<std::vector<cv::Point>> contours;
+  std::vector<cv::Vec4i> hierarchy;
+
+  cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST,
+                   cv::CHAIN_APPROX_SIMPLE);
+
+  int num_contours =
+      contours.size() >= max_candidates ? max_candidates : contours.size();
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+
+  for (int _i = 0; _i < num_contours; _i++) {
+    float ssid;
+    cv::RotatedRect box = cv::minAreaRect(contours[_i]);
+    auto array = get_mini_boxes(box, ssid);
+
+    auto box_for_unclip = array;
+    // end get_mini_box
+
+    if (ssid < min_size) {
+      continue;
+    }
+
+    float score;
+    score = box_score_fast(array, pred);
+    // end box_score_fast
+    if (score < box_thresh) {
+      continue;
+    }
+
+    // start for unclip
+    cv::RotatedRect points = unclip(box_for_unclip);
+    // end for unclip
+
+    cv::RotatedRect clipbox = points;
+    auto cliparray = get_mini_boxes(clipbox, ssid);
+
+    if (ssid < min_size + 2)
+      continue;
+
+    int dest_width = pred.cols;
+    int dest_height = pred.rows;
+    std::vector<std::vector<int>> intcliparray;
+
+    for (int num_pt = 0; num_pt < 4; num_pt++) {
+      std::vector<int> a{int(clampf(roundf(cliparray[num_pt][0] / float(width) *
+                                           float(dest_width)),
+                                    0, float(dest_width))),
+                         int(clampf(roundf(cliparray[num_pt][1] /
+                                           float(height) * float(dest_height)),
+                                    0, float(dest_height)))};
+      intcliparray.emplace_back(std::move(a));
+    }
+    boxes.emplace_back(std::move(intcliparray));
+
+  } // end for
+  return boxes;
+}
+
+int _max(int a, int b) { return a >= b ? a : b; }
+
+int _min(int a, int b) { return a >= b ? b : a; }
+
+std::vector<std::vector<std::vector<int>>>
+filter_tag_det_res(const std::vector<std::vector<std::vector<int>>> &o_boxes,
+                   float ratio_h, float ratio_w, const cv::Mat &srcimg) {
+  int oriimg_h = srcimg.rows;
+  int oriimg_w = srcimg.cols;
+  std::vector<std::vector<std::vector<int>>> boxes{o_boxes};
+  std::vector<std::vector<std::vector<int>>> root_points;
+  for (int n = 0; n < boxes.size(); n++) {
+    boxes[n] = order_points_clockwise(boxes[n]);
+    for (int m = 0; m < boxes[0].size(); m++) {
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
+
+      boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
+    }
+  }
+
+  for (int n = 0; n < boxes.size(); n++) {
+    int rect_width, rect_height;
+    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+                          pow(boxes[n][0][1] - boxes[n][1][1], 2)));
+    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+                           pow(boxes[n][0][1] - boxes[n][3][1], 2)));
+    if (rect_width <= 10 || rect_height <= 10)
+      continue;
+    root_points.push_back(boxes[n]);
+  }
+  return root_points;
+}

deploy/android_demo/app/src/main/cpp/ocr_db_post_process.h

@@ -0,0 +1,13 @@
+//
+// Created by fujiayi on 2020/7/2.
+//
+#pragma once
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+std::vector<std::vector<std::vector<int>>>
+boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap);
+
+std::vector<std::vector<std::vector<int>>>
+filter_tag_det_res(const std::vector<std::vector<std::vector<int>>> &o_boxes,
+                   float ratio_h, float ratio_w, const cv::Mat &srcimg);

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp

@@ -0,0 +1,354 @@
+//
+// Created by fujiayi on 2020/7/1.
+//
+
+#include "ocr_ppredictor.h"
+#include "common.h"
+#include "ocr_cls_process.h"
+#include "ocr_crnn_process.h"
+#include "ocr_db_post_process.h"
+#include "preprocess.h"
+
+namespace ppredictor {
+
+OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {}
+
+int OCR_PPredictor::init(const std::string &det_model_content,
+                         const std::string &rec_model_content,
+                         const std::string &cls_model_content) {
+  _det_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR, _config.mode});
+  _det_predictor->init_nb(det_model_content);
+
+  _rec_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _rec_predictor->init_nb(rec_model_content);
+
+  _cls_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _cls_predictor->init_nb(cls_model_content);
+  return RETURN_OK;
+}
+
+int OCR_PPredictor::init_from_file(const std::string &det_model_path,
+                                   const std::string &rec_model_path,
+                                   const std::string &cls_model_path) {
+  _det_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR, _config.mode});
+  _det_predictor->init_from_file(det_model_path);
+
+  _rec_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _rec_predictor->init_from_file(rec_model_path);
+
+  _cls_predictor = std::unique_ptr<PPredictor>(new PPredictor{
+      _config.use_opencl, _config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _cls_predictor->init_from_file(cls_model_path);
+  return RETURN_OK;
+}
+/**
+ * for debug use, show result of First Step
+ * @param filter_boxes
+ * @param boxes
+ * @param srcimg
+ */
+static void
+visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
+           const std::vector<std::vector<std::vector<int>>> &boxes,
+           const cv::Mat &srcimg) {
+  // visualization
+  cv::Point rook_points[filter_boxes.size()][4];
+  for (int n = 0; n < filter_boxes.size(); n++) {
+    for (int m = 0; m < filter_boxes[0].size(); m++) {
+      rook_points[n][m] =
+          cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
+    }
+  }
+
+  cv::Mat img_vis;
+  srcimg.copyTo(img_vis);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point *ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
+  }
+  // 调试用，自行替换需要修改的路径
+  cv::imwrite("/sdcard/1/vis.png", img_vis);
+}
+
+std::vector<OCRPredictResult>
+OCR_PPredictor::infer_ocr(cv::Mat &origin, int max_size_len, int run_det,
+                          int run_cls, int run_rec) {
+  LOGI("ocr cpp start *****************");
+  LOGI("ocr cpp det: %d, cls: %d, rec: %d", run_det, run_cls, run_rec);
+  std::vector<OCRPredictResult> ocr_results;
+  if (run_det) {
+    infer_det(origin, max_size_len, ocr_results);
+  }
+  if (run_rec) {
+    if (ocr_results.size() == 0) {
+      OCRPredictResult res;
+      ocr_results.emplace_back(std::move(res));
+    }
+    for (int i = 0; i < ocr_results.size(); i++) {
+      infer_rec(origin, run_cls, ocr_results[i]);
+    }
+  } else if (run_cls) {
+    ClsPredictResult cls_res = infer_cls(origin);
+    OCRPredictResult res;
+    res.cls_score = cls_res.cls_score;
+    res.cls_label = cls_res.cls_label;
+    ocr_results.push_back(res);
+  }
+
+  LOGI("ocr cpp end *****************");
+  return ocr_results;
+}
+
+cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
+                     std::vector<float> &ratio_hw) {
+  int w = img.cols;
+  int h = img.rows;
+
+  float ratio = 1.f;
+  int max_wh = w >= h ? w : h;
+  if (max_wh > max_size_len) {
+    if (h > w) {
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(h);
+    } else {
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(w);
+    }
+  }
+
+  int resize_h = static_cast<int>(float(h) * ratio);
+  int resize_w = static_cast<int>(float(w) * ratio);
+  if (resize_h % 32 == 0)
+    resize_h = resize_h;
+  else if (resize_h / 32 < 1 + 1e-5)
+    resize_h = 32;
+  else
+    resize_h = (resize_h / 32 - 1) * 32;
+
+  if (resize_w % 32 == 0)
+    resize_w = resize_w;
+  else if (resize_w / 32 < 1 + 1e-5)
+    resize_w = 32;
+  else
+    resize_w = (resize_w / 32 - 1) * 32;
+
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
+
+  ratio_hw.push_back(static_cast<float>(resize_h) / static_cast<float>(h));
+  ratio_hw.push_back(static_cast<float>(resize_w) / static_cast<float>(w));
+  return resize_img;
+}
+
+void OCR_PPredictor::infer_det(cv::Mat &origin, int max_size_len,
+                               std::vector<OCRPredictResult> &ocr_results) {
+  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+
+  PredictorInput input = _det_predictor->get_first_input();
+
+  std::vector<float> ratio_hw;
+  cv::Mat input_image = DetResizeImg(origin, max_size_len, ratio_hw);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
+
+  input.set_dims({1, 3, input_image.rows, input_image.cols});
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+  LOGI("ocr cpp det shape %d,%d", input_image.rows, input_image.cols);
+  std::vector<PredictorOutput> results = _det_predictor->infer();
+  PredictorOutput &res = results.at(0);
+  std::vector<std::vector<std::vector<int>>> filtered_box =
+      calc_filtered_boxes(res.get_float_data(), res.get_size(),
+                          input_image.rows, input_image.cols, origin);
+  LOGI("ocr cpp det Filter_box size %ld", filtered_box.size());
+
+  for (int i = 0; i < filtered_box.size(); i++) {
+    LOGI("ocr cpp box  %d,%d,%d,%d,%d,%d,%d,%d", filtered_box[i][0][0],
+         filtered_box[i][0][1], filtered_box[i][1][0], filtered_box[i][1][1],
+         filtered_box[i][2][0], filtered_box[i][2][1], filtered_box[i][3][0],
+         filtered_box[i][3][1]);
+    OCRPredictResult res;
+    res.points = filtered_box[i];
+    ocr_results.push_back(res);
+  }
+}
+
+void OCR_PPredictor::infer_rec(const cv::Mat &origin_img, int run_cls,
+                               OCRPredictResult &ocr_result) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  std::vector<int64_t> dims = {1, 3, 0, 0};
+
+  PredictorInput input = _rec_predictor->get_first_input();
+
+  const std::vector<std::vector<int>> &box = ocr_result.points;
+  cv::Mat crop_img;
+  if (box.size() > 0) {
+    crop_img = get_rotate_crop_image(origin_img, box);
+  } else {
+    crop_img = origin_img;
+  }
+
+  if (run_cls) {
+    ClsPredictResult cls_res = infer_cls(crop_img);
+    crop_img = cls_res.img;
+    ocr_result.cls_score = cls_res.cls_score;
+    ocr_result.cls_label = cls_res.cls_label;
+  }
+
+  float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+  cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
+
+  dims[2] = input_image.rows;
+  dims[3] = input_image.cols;
+  input.set_dims(dims);
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+
+  std::vector<PredictorOutput> results = _rec_predictor->infer();
+  const float *predict_batch = results.at(0).get_float_data();
+  const std::vector<int64_t> predict_shape = results.at(0).get_shape();
+
+  // ctc decode
+  int argmax_idx;
+  int last_index = 0;
+  float score = 0.f;
+  int count = 0;
+  float max_value = 0.0f;
+
+  for (int n = 0; n < predict_shape[1]; n++) {
+    argmax_idx = int(argmax(&predict_batch[n * predict_shape[2]],
+                            &predict_batch[(n + 1) * predict_shape[2]]));
+    max_value =
+        float(*std::max_element(&predict_batch[n * predict_shape[2]],
+                                &predict_batch[(n + 1) * predict_shape[2]]));
+    if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
+      score += max_value;
+      count += 1;
+      ocr_result.word_index.push_back(argmax_idx);
+    }
+    last_index = argmax_idx;
+  }
+  score /= count;
+  ocr_result.score = score;
+  LOGI("ocr cpp rec word size %ld", count);
+}
+
+ClsPredictResult OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  std::vector<int64_t> dims = {1, 3, 0, 0};
+
+  PredictorInput input = _cls_predictor->get_first_input();
+
+  cv::Mat input_image = cls_resize_img(img);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
+
+  dims[2] = input_image.rows;
+  dims[3] = input_image.cols;
+  input.set_dims(dims);
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+
+  std::vector<PredictorOutput> results = _cls_predictor->infer();
+
+  const float *scores = results.at(0).get_float_data();
+  float score = 0;
+  int label = 0;
+  for (int64_t i = 0; i < results.at(0).get_size(); i++) {
+    LOGI("ocr cpp cls output scores [%f]", scores[i]);
+    if (scores[i] > score) {
+      score = scores[i];
+      label = i;
+    }
+  }
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  if (label % 2 == 1 && score > thresh) {
+    cv::rotate(srcimg, srcimg, 1);
+  }
+  ClsPredictResult res;
+  res.cls_label = label;
+  res.cls_score = score;
+  res.img = srcimg;
+  LOGI("ocr cpp cls word cls %ld, %f", label, score);
+  return res;
+}
+
+std::vector<std::vector<std::vector<int>>>
+OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size,
+                                    int output_height, int output_width,
+                                    const cv::Mat &origin) {
+  const double threshold = 0.3;
+  const double maxvalue = 1;
+
+  cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
+  memcpy(pred_map.data, pred, pred_size * sizeof(float));
+  cv::Mat cbuf_map;
+  pred_map.convertTo(cbuf_map, CV_8UC1);
+
+  cv::Mat bit_map;
+  cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
+
+  std::vector<std::vector<std::vector<int>>> boxes =
+      boxes_from_bitmap(pred_map, bit_map);
+  float ratio_h = output_height * 1.0f / origin.rows;
+  float ratio_w = output_width * 1.0f / origin.cols;
+  std::vector<std::vector<std::vector<int>>> filter_boxes =
+      filter_tag_det_res(boxes, ratio_h, ratio_w, origin);
+  return filter_boxes;
+}
+
+std::vector<int>
+OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
+  const int *rec_idx = res.get_int_data();
+  const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
+
+  std::vector<int> pred_idx;
+  for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
+    pred_idx.emplace_back(rec_idx[n]);
+  }
+  return pred_idx;
+}
+
+float OCR_PPredictor::postprocess_rec_score(const PredictorOutput &res) {
+  const float *predict_batch = res.get_float_data();
+  const std::vector<int64_t> predict_shape = res.get_shape();
+  const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
+  int blank = predict_shape[1];
+  float score = 0.f;
+  int count = 0;
+  for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
+    int argmax_idx = argmax(predict_batch + n * predict_shape[1],
+                            predict_batch + (n + 1) * predict_shape[1]);
+    float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
+    if (blank - 1 - argmax_idx > 1e-5) {
+      score += max_value;
+      count += 1;
+    }
+  }
+  if (count == 0) {
+    LOGE("calc score count 0");
+  } else {
+    score /= count;
+  }
+  LOGI("calc score: %f", score);
+  return score;
+}
+
+NET_TYPE OCR_PPredictor::get_net_flag() const { return NET_OCR; }
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h

@@ -0,0 +1,131 @@
+//
+// Created by fujiayi on 2020/7/1.
+//
+
+#pragma once
+
+#include "ppredictor.h"
+#include <opencv2/opencv.hpp>
+#include <paddle_api.h>
+#include <string>
+
+namespace ppredictor {
+
+/**
+ * Config
+ */
+struct OCR_Config {
+  int use_opencl = 0;
+  int thread_num = 4; // Thread num
+  paddle::lite_api::PowerMode mode =
+      paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
+};
+
+/**
+ * Polygons Result
+ */
+struct OCRPredictResult {
+  std::vector<int> word_index;
+  std::vector<std::vector<int>> points;
+  float score;
+  float cls_score;
+  int cls_label = -1;
+};
+
+struct ClsPredictResult {
+  float cls_score;
+  int cls_label = -1;
+  cv::Mat img;
+};
+/**
+ * OCR there are 2 models
+ * 1. First model（det），select polygons to show where are the texts
+ * 2. crop from the origin images, use these polygons to infer
+ */
+class OCR_PPredictor : public PPredictor_Interface {
+public:
+  OCR_PPredictor(const OCR_Config &config);
+
+  virtual ~OCR_PPredictor() {}
+
+  /**
+   * 初始化二个模型的Predictor
+   * @param det_model_content
+   * @param rec_model_content
+   * @return
+   */
+  int init(const std::string &det_model_content,
+           const std::string &rec_model_content,
+           const std::string &cls_model_content);
+  int init_from_file(const std::string &det_model_path,
+                     const std::string &rec_model_path,
+                     const std::string &cls_model_path);
+  /**
+   * Return OCR result
+   * @param dims
+   * @param input_data
+   * @param input_len
+   * @param net_flag
+   * @param origin
+   * @return
+   */
+  virtual std::vector<OCRPredictResult> infer_ocr(cv::Mat &origin,
+                                                  int max_size_len, int run_det,
+                                                  int run_cls, int run_rec);
+
+  virtual NET_TYPE get_net_flag() const;
+
+private:
+  /**
+   * calculate polygons from the result image of first model
+   * @param pred
+   * @param output_height
+   * @param output_width
+   * @param origin
+   * @return
+   */
+  std::vector<std::vector<std::vector<int>>>
+  calc_filtered_boxes(const float *pred, int pred_size, int output_height,
+                      int output_width, const cv::Mat &origin);
+
+  void infer_det(cv::Mat &origin, int max_side_len,
+                 std::vector<OCRPredictResult> &ocr_results);
+  /**
+   * infer for rec model
+   *
+   * @param boxes
+   * @param origin
+   * @return
+   */
+  void infer_rec(const cv::Mat &origin, int run_cls,
+                 OCRPredictResult &ocr_result);
+
+  /**
+   * infer for cls model
+   *
+   * @param boxes
+   * @param origin
+   * @return
+   */
+  ClsPredictResult infer_cls(const cv::Mat &origin, float thresh = 0.9);
+
+  /**
+   * Postprocess or second model to extract text
+   * @param res
+   * @return
+   */
+  std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
+
+  /**
+   * calculate confidence of second model text result
+   * @param res
+   * @return
+   */
+  float postprocess_rec_score(const PredictorOutput &res);
+
+  std::unique_ptr<PPredictor> _det_predictor;
+  std::unique_ptr<PPredictor> _rec_predictor;
+  std::unique_ptr<PPredictor> _cls_predictor;
+  OCR_Config _config;
+};
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/ppredictor.cpp

@@ -0,0 +1,99 @@
+#include "ppredictor.h"
+#include "common.h"
+
+namespace ppredictor {
+PPredictor::PPredictor(int use_opencl, int thread_num, int net_flag,
+                       paddle::lite_api::PowerMode mode)
+    : _use_opencl(use_opencl), _thread_num(thread_num), _net_flag(net_flag),
+      _mode(mode) {}
+
+int PPredictor::init_nb(const std::string &model_content) {
+  paddle::lite_api::MobileConfig config;
+  config.set_model_from_buffer(model_content);
+  return _init(config);
+}
+
+int PPredictor::init_from_file(const std::string &model_content) {
+  paddle::lite_api::MobileConfig config;
+  config.set_model_from_file(model_content);
+  return _init(config);
+}
+
+template <typename ConfigT> int PPredictor::_init(ConfigT &config) {
+  bool is_opencl_backend_valid =
+      paddle::lite_api::IsOpenCLBackendValid(/*check_fp16_valid = false*/);
+  if (is_opencl_backend_valid) {
+    if (_use_opencl != 0) {
+      // Make sure you have write permission of the binary path.
+      // We strongly recommend each model has a unique binary name.
+      const std::string bin_path = "/data/local/tmp/";
+      const std::string bin_name = "lite_opencl_kernel.bin";
+      config.set_opencl_binary_path_name(bin_path, bin_name);
+
+      // opencl tune option
+      // CL_TUNE_NONE: 0
+      // CL_TUNE_RAPID: 1
+      // CL_TUNE_NORMAL: 2
+      // CL_TUNE_EXHAUSTIVE: 3
+      const std::string tuned_path = "/data/local/tmp/";
+      const std::string tuned_name = "lite_opencl_tuned.bin";
+      config.set_opencl_tune(paddle::lite_api::CL_TUNE_NORMAL, tuned_path,
+                             tuned_name);
+
+      // opencl precision option
+      // CL_PRECISION_AUTO: 0, first fp16 if valid, default
+      // CL_PRECISION_FP32: 1, force fp32
+      // CL_PRECISION_FP16: 2, force fp16
+      config.set_opencl_precision(paddle::lite_api::CL_PRECISION_FP32);
+      LOGI("ocr cpp device: running on gpu.");
+    }
+  } else {
+    LOGI("ocr cpp device: running on cpu.");
+    // you can give backup cpu nb model instead
+    // config.set_model_from_file(cpu_nb_model_dir);
+  }
+  config.set_threads(_thread_num);
+  config.set_power_mode(_mode);
+  _predictor = paddle::lite_api::CreatePaddlePredictor(config);
+  LOGI("ocr cpp paddle instance created");
+  return RETURN_OK;
+}
+
+PredictorInput PPredictor::get_input(int index) {
+  PredictorInput input{_predictor->GetInput(index), index, _net_flag};
+  _is_input_get = true;
+  return input;
+}
+
+std::vector<PredictorInput> PPredictor::get_inputs(int num) {
+  std::vector<PredictorInput> results;
+  for (int i = 0; i < num; i++) {
+    results.emplace_back(get_input(i));
+  }
+  return results;
+}
+
+PredictorInput PPredictor::get_first_input() { return get_input(0); }
+
+std::vector<PredictorOutput> PPredictor::infer() {
+  LOGI("ocr cpp infer Run start %d", _net_flag);
+  std::vector<PredictorOutput> results;
+  if (!_is_input_get) {
+    return results;
+  }
+  _predictor->Run();
+  LOGI("ocr cpp infer Run end");
+
+  for (int i = 0; i < _predictor->GetOutputNames().size(); i++) {
+    std::unique_ptr<const paddle::lite_api::Tensor> output_tensor =
+        _predictor->GetOutput(i);
+    LOGI("ocr cpp output tensor[%d] size %ld", i,
+         product(output_tensor->shape()));
+    PredictorOutput result{std::move(output_tensor), i, _net_flag};
+    results.emplace_back(std::move(result));
+  }
+  return results;
+}
+
+NET_TYPE PPredictor::get_net_flag() const { return (NET_TYPE)_net_flag; }
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/ppredictor.h

@@ -0,0 +1,64 @@
+#pragma once
+
+#include "paddle_api.h"
+#include "predictor_input.h"
+#include "predictor_output.h"
+
+namespace ppredictor {
+
+/**
+ * PaddleLite Preditor Common Interface
+ */
+class PPredictor_Interface {
+public:
+  virtual ~PPredictor_Interface() {}
+
+  virtual NET_TYPE get_net_flag() const = 0;
+};
+
+/**
+ * Common Predictor
+ */
+class PPredictor : public PPredictor_Interface {
+public:
+  PPredictor(
+      int use_opencl, int thread_num, int net_flag = 0,
+      paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
+
+  virtual ~PPredictor() {}
+
+  /**
+   * init paddlitelite opt model，nb format ，or use ini_paddle
+   * @param model_content
+   * @return 0
+   */
+  virtual int init_nb(const std::string &model_content);
+
+  virtual int init_from_file(const std::string &model_content);
+
+  std::vector<PredictorOutput> infer();
+
+  std::shared_ptr<paddle::lite_api::PaddlePredictor> get_predictor() {
+    return _predictor;
+  }
+
+  virtual std::vector<PredictorInput> get_inputs(int num);
+
+  virtual PredictorInput get_input(int index);
+
+  virtual PredictorInput get_first_input();
+
+  virtual NET_TYPE get_net_flag() const;
+
+protected:
+  template <typename ConfigT> int _init(ConfigT &config);
+
+private:
+  int _use_opencl;
+  int _thread_num;
+  paddle::lite_api::PowerMode _mode;
+  std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;
+  bool _is_input_get = false;
+  int _net_flag;
+};
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/predictor_input.cpp

@@ -0,0 +1,28 @@
+#include "predictor_input.h"
+
+namespace ppredictor {
+
+void PredictorInput::set_dims(std::vector<int64_t> dims) {
+  // yolov3
+  if (_net_flag == 101 && _index == 1) {
+    _tensor->Resize({1, 2});
+    _tensor->mutable_data<int>()[0] = (int)dims.at(2);
+    _tensor->mutable_data<int>()[1] = (int)dims.at(3);
+  } else {
+    _tensor->Resize(dims);
+  }
+  _is_dims_set = true;
+}
+
+float *PredictorInput::get_mutable_float_data() {
+  if (!_is_dims_set) {
+    LOGE("PredictorInput::set_dims is not called");
+  }
+  return _tensor->mutable_data<float>();
+}
+
+void PredictorInput::set_data(const float *input_data, int input_float_len) {
+  float *input_raw_data = get_mutable_float_data();
+  memcpy(input_raw_data, input_data, input_float_len * sizeof(float));
+}
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/predictor_input.h

@@ -0,0 +1,26 @@
+#pragma once
+
+#include "common.h"
+#include <paddle_api.h>
+#include <vector>
+
+namespace ppredictor {
+class PredictorInput {
+public:
+  PredictorInput(std::unique_ptr<paddle::lite_api::Tensor> &&tensor, int index,
+                 int net_flag)
+      : _tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {}
+
+  void set_dims(std::vector<int64_t> dims);
+
+  float *get_mutable_float_data();
+
+  void set_data(const float *input_data, int input_float_len);
+
+private:
+  std::unique_ptr<paddle::lite_api::Tensor> _tensor;
+  bool _is_dims_set = false;
+  int _index;
+  int _net_flag;
+};
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/predictor_output.cpp

@@ -0,0 +1,26 @@
+#include "predictor_output.h"
+namespace ppredictor {
+const float *PredictorOutput::get_float_data() const {
+  return _tensor->data<float>();
+}
+
+const int *PredictorOutput::get_int_data() const {
+  return _tensor->data<int>();
+}
+
+const std::vector<std::vector<uint64_t>> PredictorOutput::get_lod() const {
+  return _tensor->lod();
+}
+
+int64_t PredictorOutput::get_size() const {
+  if (_net_flag == NET_OCR) {
+    return _tensor->shape().at(2) * _tensor->shape().at(3);
+  } else {
+    return product(_tensor->shape());
+  }
+}
+
+const std::vector<int64_t> PredictorOutput::get_shape() const {
+  return _tensor->shape();
+}
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/predictor_output.h

@@ -0,0 +1,31 @@
+#pragma once
+
+#include "common.h"
+#include <paddle_api.h>
+#include <vector>
+
+namespace ppredictor {
+class PredictorOutput {
+public:
+  PredictorOutput() {}
+  PredictorOutput(std::unique_ptr<const paddle::lite_api::Tensor> &&tensor,
+                  int index, int net_flag)
+      : _tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {}
+
+  const float *get_float_data() const;
+  const int *get_int_data() const;
+  int64_t get_size() const;
+  const std::vector<std::vector<uint64_t>> get_lod() const;
+  const std::vector<int64_t> get_shape() const;
+
+  std::vector<float> data;    // return float, or use data_int
+  std::vector<int> data_int;  // several layers return int ，or use data
+  std::vector<int64_t> shape; // PaddleLite output shape
+  std::vector<std::vector<uint64_t>> lod; // PaddleLite output lod
+
+private:
+  std::unique_ptr<const paddle::lite_api::Tensor> _tensor;
+  int _index;
+  int _net_flag;
+};
+} // namespace ppredictor

deploy/android_demo/app/src/main/cpp/preprocess.cpp

@@ -0,0 +1,82 @@
+#include "preprocess.h"
+#include <android/bitmap.h>
+
+cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
+  AndroidBitmapInfo info;
+  int result = AndroidBitmap_getInfo(env, bitmap, &info);
+  if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
+    LOGE("AndroidBitmap_getInfo failed, result: %d", result);
+    return cv::Mat{};
+  }
+  if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
+    LOGE("Bitmap format is not RGBA_8888 !");
+    return cv::Mat{};
+  }
+  unsigned char *srcData = NULL;
+  AndroidBitmap_lockPixels(env, bitmap, (void **)&srcData);
+  cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
+  memcpy(mat.data, srcData, info.height * info.width * 4);
+  AndroidBitmap_unlockPixels(env, bitmap);
+  cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
+  /**
+  if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
+      LOGE("Write image failed " );
+  }
+   */
+
+  return mat;
+}
+
+cv::Mat resize_img(const cv::Mat &img, int height, int width) {
+  if (img.rows == height && img.cols == width) {
+    return img;
+  }
+  cv::Mat new_img;
+  cv::resize(img, new_img, cv::Size(height, width));
+  return new_img;
+}
+
+// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
+void neon_mean_scale(const float *din, float *dout, int size,
+                     const std::vector<float> &mean,
+                     const std::vector<float> &scale) {
+  if (mean.size() != 3 || scale.size() != 3) {
+    LOGE("[ERROR] mean or scale size must equal to 3");
+    return;
+  }
+
+  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
+  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
+  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
+  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
+  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
+  float32x4_t vscale2 = vdupq_n_f32(scale[2]);
+
+  float *dout_c0 = dout;
+  float *dout_c1 = dout + size;
+  float *dout_c2 = dout + size * 2;
+
+  int i = 0;
+  for (; i < size - 3; i += 4) {
+    float32x4x3_t vin3 = vld3q_f32(din);
+    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
+    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
+    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
+    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
+    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
+    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
+    vst1q_f32(dout_c0, vs0);
+    vst1q_f32(dout_c1, vs1);
+    vst1q_f32(dout_c2, vs2);
+
+    din += 12;
+    dout_c0 += 4;
+    dout_c1 += 4;
+    dout_c2 += 4;
+  }
+  for (; i < size; i++) {
+    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
+    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
+    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
+  }
+}

deploy/android_demo/app/src/main/cpp/preprocess.h

@@ -0,0 +1,12 @@
+#pragma once
+
+#include "common.h"
+#include <jni.h>
+#include <opencv2/opencv.hpp>
+cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap);
+
+cv::Mat resize_img(const cv::Mat &img, int height, int width);
+
+void neon_mean_scale(const float *din, float *dout, int size,
+                     const std::vector<float> &mean,
+                     const std::vector<float> &scale);

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/AppCompatPreferenceActivity.java

@@ -0,0 +1,128 @@
+/*
+ * Copyright (C) 2014 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.res.Configuration;
+import android.os.Bundle;
+import android.preference.PreferenceActivity;
+import android.view.MenuInflater;
+import android.view.View;
+import android.view.ViewGroup;
+
+import androidx.annotation.LayoutRes;
+import androidx.annotation.Nullable;
+import androidx.appcompat.app.ActionBar;
+import androidx.appcompat.app.AppCompatDelegate;
+import androidx.appcompat.widget.Toolbar;
+
+/**
+ * A {@link PreferenceActivity} which implements and proxies the necessary calls
+ * to be used with AppCompat.
+ * <p>
+ * This technique can be used with an {@link android.app.Activity} class, not just
+ * {@link PreferenceActivity}.
+ */
+public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
+    private AppCompatDelegate mDelegate;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        getDelegate().installViewFactory();
+        getDelegate().onCreate(savedInstanceState);
+        super.onCreate(savedInstanceState);
+    }
+
+    @Override
+    protected void onPostCreate(Bundle savedInstanceState) {
+        super.onPostCreate(savedInstanceState);
+        getDelegate().onPostCreate(savedInstanceState);
+    }
+
+    public ActionBar getSupportActionBar() {
+        return getDelegate().getSupportActionBar();
+    }
+
+    public void setSupportActionBar(@Nullable Toolbar toolbar) {
+        getDelegate().setSupportActionBar(toolbar);
+    }
+
+    @Override
+    public MenuInflater getMenuInflater() {
+        return getDelegate().getMenuInflater();
+    }
+
+    @Override
+    public void setContentView(@LayoutRes int layoutResID) {
+        getDelegate().setContentView(layoutResID);
+    }
+
+    @Override
+    public void setContentView(View view) {
+        getDelegate().setContentView(view);
+    }
+
+    @Override
+    public void setContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().setContentView(view, params);
+    }
+
+    @Override
+    public void addContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().addContentView(view, params);
+    }
+
+    @Override
+    protected void onPostResume() {
+        super.onPostResume();
+        getDelegate().onPostResume();
+    }
+
+    @Override
+    protected void onTitleChanged(CharSequence title, int color) {
+        super.onTitleChanged(title, color);
+        getDelegate().setTitle(title);
+    }
+
+    @Override
+    public void onConfigurationChanged(Configuration newConfig) {
+        super.onConfigurationChanged(newConfig);
+        getDelegate().onConfigurationChanged(newConfig);
+    }
+
+    @Override
+    protected void onStop() {
+        super.onStop();
+        getDelegate().onStop();
+    }
+
+    @Override
+    protected void onDestroy() {
+        super.onDestroy();
+        getDelegate().onDestroy();
+    }
+
+    public void invalidateOptionsMenu() {
+        getDelegate().invalidateOptionsMenu();
+    }
+
+    private AppCompatDelegate getDelegate() {
+        if (mDelegate == null) {
+            mDelegate = AppCompatDelegate.create(this, null);
+        }
+        return mDelegate;
+    }
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java

@@ -0,0 +1,520 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.Manifest;
+import android.app.ProgressDialog;
+import android.content.ContentResolver;
+import android.content.Context;
+import android.content.Intent;
+import android.content.SharedPreferences;
+import android.content.pm.PackageManager;
+import android.database.Cursor;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.graphics.drawable.BitmapDrawable;
+import android.media.ExifInterface;
+import android.content.res.AssetManager;
+import android.media.FaceDetector;
+import android.net.Uri;
+import android.os.Bundle;
+import android.os.Environment;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.os.Message;
+import android.preference.PreferenceManager;
+import android.provider.MediaStore;
+import android.text.method.ScrollingMovementMethod;
+import android.util.Log;
+import android.view.Menu;
+import android.view.MenuInflater;
+import android.view.MenuItem;
+import android.view.View;
+import android.widget.CheckBox;
+import android.widget.ImageView;
+import android.widget.Spinner;
+import android.widget.TextView;
+import android.widget.Toast;
+
+import androidx.annotation.NonNull;
+import androidx.appcompat.app.AppCompatActivity;
+import androidx.core.app.ActivityCompat;
+import androidx.core.content.ContextCompat;
+import androidx.core.content.FileProvider;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.InputStream;
+import java.text.SimpleDateFormat;
+import java.util.Date;
+
+public class MainActivity extends AppCompatActivity {
+    private static final String TAG = MainActivity.class.getSimpleName();
+    public static final int OPEN_GALLERY_REQUEST_CODE = 0;
+    public static final int TAKE_PHOTO_REQUEST_CODE = 1;
+
+    public static final int REQUEST_LOAD_MODEL = 0;
+    public static final int REQUEST_RUN_MODEL = 1;
+    public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
+    public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
+    public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
+    public static final int RESPONSE_RUN_MODEL_FAILED = 3;
+
+    protected ProgressDialog pbLoadModel = null;
+    protected ProgressDialog pbRunModel = null;
+
+    protected Handler receiver = null; // Receive messages from worker thread
+    protected Handler sender = null; // Send command to worker thread
+    protected HandlerThread worker = null; // Worker thread to load&run model
+
+    // UI components of object detection
+    protected TextView tvInputSetting;
+    protected TextView tvStatus;
+    protected ImageView ivInputImage;
+    protected TextView tvOutputResult;
+    protected TextView tvInferenceTime;
+    protected CheckBox cbOpencl;
+    protected Spinner spRunMode;
+
+    // Model settings of ocr
+    protected String modelPath = "";
+    protected String labelPath = "";
+    protected String imagePath = "";
+    protected int cpuThreadNum = 1;
+    protected String cpuPowerMode = "";
+    protected int detLongSize = 960;
+    protected float scoreThreshold = 0.1f;
+    private String currentPhotoPath;
+    private AssetManager assetManager = null;
+
+    protected Predictor predictor = new Predictor();
+
+    private Bitmap cur_predict_image = null;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+
+        // Clear all setting items to avoid app crashing due to the incorrect settings
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        SharedPreferences.Editor editor = sharedPreferences.edit();
+        editor.clear();
+        editor.apply();
+
+        // Setup the UI components
+        tvInputSetting = findViewById(R.id.tv_input_setting);
+        cbOpencl = findViewById(R.id.cb_opencl);
+        tvStatus = findViewById(R.id.tv_model_img_status);
+        ivInputImage = findViewById(R.id.iv_input_image);
+        tvInferenceTime = findViewById(R.id.tv_inference_time);
+        tvOutputResult = findViewById(R.id.tv_output_result);
+        spRunMode = findViewById(R.id.sp_run_mode);
+        tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
+        tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
+
+        // Prepare the worker thread for mode loading and inference
+        receiver = new Handler() {
+            @Override
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case RESPONSE_LOAD_MODEL_SUCCESSED:
+                        if (pbLoadModel != null && pbLoadModel.isShowing()) {
+                            pbLoadModel.dismiss();
+                        }
+                        onLoadModelSuccessed();
+                        break;
+                    case RESPONSE_LOAD_MODEL_FAILED:
+                        if (pbLoadModel != null && pbLoadModel.isShowing()) {
+                            pbLoadModel.dismiss();
+                        }
+                        Toast.makeText(MainActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
+                        onLoadModelFailed();
+                        break;
+                    case RESPONSE_RUN_MODEL_SUCCESSED:
+                        if (pbRunModel != null && pbRunModel.isShowing()) {
+                            pbRunModel.dismiss();
+                        }
+                        onRunModelSuccessed();
+                        break;
+                    case RESPONSE_RUN_MODEL_FAILED:
+                        if (pbRunModel != null && pbRunModel.isShowing()) {
+                            pbRunModel.dismiss();
+                        }
+                        Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
+                        onRunModelFailed();
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+
+        worker = new HandlerThread("Predictor Worker");
+        worker.start();
+        sender = new Handler(worker.getLooper()) {
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case REQUEST_LOAD_MODEL:
+                        // Load model and reload test image
+                        if (onLoadModel()) {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_FAILED);
+                        }
+                        break;
+                    case REQUEST_RUN_MODEL:
+                        // Run model if model is loaded
+                        if (onRunModel()) {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_FAILED);
+                        }
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        boolean settingsChanged = false;
+        boolean model_settingsChanged = false;
+        String model_path = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
+                getString(R.string.LABEL_PATH_DEFAULT));
+        String image_path = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
+                getString(R.string.IMAGE_PATH_DEFAULT));
+        model_settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
+        settingsChanged |= !label_path.equalsIgnoreCase(labelPath);
+        settingsChanged |= !image_path.equalsIgnoreCase(imagePath);
+        int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
+                getString(R.string.CPU_THREAD_NUM_DEFAULT)));
+        model_settingsChanged |= cpu_thread_num != cpuThreadNum;
+        String cpu_power_mode =
+                sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
+                        getString(R.string.CPU_POWER_MODE_DEFAULT));
+        model_settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
+
+        int det_long_size = Integer.parseInt(sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
+                getString(R.string.DET_LONG_SIZE_DEFAULT)));
+        settingsChanged |= det_long_size != detLongSize;
+        float score_threshold =
+                Float.parseFloat(sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
+                        getString(R.string.SCORE_THRESHOLD_DEFAULT)));
+        settingsChanged |= scoreThreshold != score_threshold;
+        if (settingsChanged) {
+            labelPath = label_path;
+            imagePath = image_path;
+            detLongSize = det_long_size;
+            scoreThreshold = score_threshold;
+            set_img();
+        }
+        if (model_settingsChanged) {
+            modelPath = model_path;
+            cpuThreadNum = cpu_thread_num;
+            cpuPowerMode = cpu_power_mode;
+            // Update UI
+            tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
+            tvInputSetting.scrollTo(0, 0);
+            // Reload model if configure has been changed
+            loadModel();
+        }
+    }
+
+    public void loadModel() {
+        pbLoadModel = ProgressDialog.show(this, "", "loading model...", false, false);
+        sender.sendEmptyMessage(REQUEST_LOAD_MODEL);
+    }
+
+    public void runModel() {
+        pbRunModel = ProgressDialog.show(this, "", "running model...", false, false);
+        sender.sendEmptyMessage(REQUEST_RUN_MODEL);
+    }
+
+    public boolean onLoadModel() {
+        if (predictor.isLoaded()) {
+            predictor.releaseModel();
+        }
+        return predictor.init(MainActivity.this, modelPath, labelPath, cbOpencl.isChecked() ? 1 : 0, cpuThreadNum,
+                cpuPowerMode,
+                detLongSize, scoreThreshold);
+    }
+
+    public boolean onRunModel() {
+        String run_mode = spRunMode.getSelectedItem().toString();
+        int run_det = run_mode.contains("检测") ? 1 : 0;
+        int run_cls = run_mode.contains("分类") ? 1 : 0;
+        int run_rec = run_mode.contains("识别") ? 1 : 0;
+        return predictor.isLoaded() && predictor.runModel(run_det, run_cls, run_rec);
+    }
+
+    public void onLoadModelSuccessed() {
+        // Load test image from path and run model
+        tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
+        tvInputSetting.scrollTo(0, 0);
+        tvStatus.setText("STATUS: load model succeeded");
+
+    }
+
+    public void onLoadModelFailed() {
+        tvStatus.setText("STATUS: load model failed");
+    }
+
+    public void onRunModelSuccessed() {
+        tvStatus.setText("STATUS: run model succeeded");
+        // Obtain results and update UI
+        tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
+        Bitmap outputImage = predictor.outputImage();
+        if (outputImage != null) {
+            ivInputImage.setImageBitmap(outputImage);
+        }
+        tvOutputResult.setText(predictor.outputResult());
+        tvOutputResult.scrollTo(0, 0);
+    }
+
+    public void onRunModelFailed() {
+        tvStatus.setText("STATUS: run model failed");
+    }
+
+    public void set_img() {
+        // Load test image from path and run model
+        try {
+            assetManager = getAssets();
+            InputStream in = assetManager.open(imagePath);
+            Bitmap bmp = BitmapFactory.decodeStream(in);
+            cur_predict_image = bmp;
+            ivInputImage.setImageBitmap(bmp);
+        } catch (IOException e) {
+            Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
+            e.printStackTrace();
+        }
+    }
+
+    public void onSettingsClicked() {
+        startActivity(new Intent(MainActivity.this, SettingsActivity.class));
+    }
+
+    @Override
+    public boolean onCreateOptionsMenu(Menu menu) {
+        MenuInflater inflater = getMenuInflater();
+        inflater.inflate(R.menu.menu_action_options, menu);
+        return true;
+    }
+
+    public boolean onPrepareOptionsMenu(Menu menu) {
+        boolean isLoaded = predictor.isLoaded();
+        return super.onPrepareOptionsMenu(menu);
+    }
+
+    @Override
+    public boolean onOptionsItemSelected(MenuItem item) {
+        switch (item.getItemId()) {
+            case android.R.id.home:
+                finish();
+                break;
+            case R.id.settings:
+                if (requestAllPermissions()) {
+                    // Make sure we have SDCard r&w permissions to load model from SDCard
+                    onSettingsClicked();
+                }
+                break;
+        }
+        return super.onOptionsItemSelected(item);
+    }
+
+    @Override
+    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
+                                           @NonNull int[] grantResults) {
+        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
+        if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
+            Toast.makeText(this, "Permission Denied", Toast.LENGTH_SHORT).show();
+        }
+    }
+
+    private boolean requestAllPermissions() {
+        if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE)
+                != PackageManager.PERMISSION_GRANTED || ContextCompat.checkSelfPermission(this,
+                Manifest.permission.CAMERA)
+                != PackageManager.PERMISSION_GRANTED) {
+            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
+                            Manifest.permission.CAMERA},
+                    0);
+            return false;
+        }
+        return true;
+    }
+
+    private void openGallery() {
+        Intent intent = new Intent(Intent.ACTION_PICK, null);
+        intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");
+        startActivityForResult(intent, OPEN_GALLERY_REQUEST_CODE);
+    }
+
+    private void takePhoto() {
+        Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
+        // Ensure that there's a camera activity to handle the intent
+        if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
+            // Create the File where the photo should go
+            File photoFile = null;
+            try {
+                photoFile = createImageFile();
+            } catch (IOException ex) {
+                Log.e("MainActitity", ex.getMessage(), ex);
+                Toast.makeText(MainActivity.this,
+                        "Create Camera temp file failed: " + ex.getMessage(), Toast.LENGTH_SHORT).show();
+            }
+            // Continue only if the File was successfully created
+            if (photoFile != null) {
+                Log.i(TAG, "FILEPATH " + getExternalFilesDir("Pictures").getAbsolutePath());
+                Uri photoURI = FileProvider.getUriForFile(this,
+                        "com.baidu.paddle.lite.demo.ocr.fileprovider",
+                        photoFile);
+                currentPhotoPath = photoFile.getAbsolutePath();
+                takePictureIntent.putExtra(MediaStore.EXTRA_OUTPUT, photoURI);
+                startActivityForResult(takePictureIntent, TAKE_PHOTO_REQUEST_CODE);
+                Log.i(TAG, "startActivityForResult finished");
+            }
+        }
+
+    }
+
+    private File createImageFile() throws IOException {
+        // Create an image file name
+        String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
+        String imageFileName = "JPEG_" + timeStamp + "_";
+        File storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES);
+        File image = File.createTempFile(
+                imageFileName,  /* prefix */
+                ".bmp",         /* suffix */
+                storageDir      /* directory */
+        );
+
+        return image;
+    }
+
+    @Override
+    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
+        super.onActivityResult(requestCode, resultCode, data);
+        if (resultCode == RESULT_OK) {
+            switch (requestCode) {
+                case OPEN_GALLERY_REQUEST_CODE:
+                    if (data == null) {
+                        break;
+                    }
+                    try {
+                        ContentResolver resolver = getContentResolver();
+                        Uri uri = data.getData();
+                        Bitmap image = MediaStore.Images.Media.getBitmap(resolver, uri);
+                        String[] proj = {MediaStore.Images.Media.DATA};
+                        Cursor cursor = managedQuery(uri, proj, null, null, null);
+                        cursor.moveToFirst();
+                        if (image != null) {
+                            cur_predict_image = image;
+                            ivInputImage.setImageBitmap(image);
+                        }
+                    } catch (IOException e) {
+                        Log.e(TAG, e.toString());
+                    }
+                    break;
+                case TAKE_PHOTO_REQUEST_CODE:
+                    if (currentPhotoPath != null) {
+                        ExifInterface exif = null;
+                        try {
+                            exif = new ExifInterface(currentPhotoPath);
+                        } catch (IOException e) {
+                            e.printStackTrace();
+                        }
+                        int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION,
+                                ExifInterface.ORIENTATION_UNDEFINED);
+                        Log.i(TAG, "rotation " + orientation);
+                        Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
+                        image = Utils.rotateBitmap(image, orientation);
+                        if (image != null) {
+                            cur_predict_image = image;
+                            ivInputImage.setImageBitmap(image);
+                        }
+                    } else {
+                        Log.e(TAG, "currentPhotoPath is null");
+                    }
+                    break;
+                default:
+                    break;
+            }
+        }
+    }
+
+    public void btn_reset_img_click(View view) {
+        ivInputImage.setImageBitmap(cur_predict_image);
+    }
+
+    public void cb_opencl_click(View view) {
+        tvStatus.setText("STATUS: load model ......");
+        loadModel();
+    }
+
+    public void btn_run_model_click(View view) {
+        Bitmap image = ((BitmapDrawable) ivInputImage.getDrawable()).getBitmap();
+        if (image == null) {
+            tvStatus.setText("STATUS: image is not exists");
+        } else if (!predictor.isLoaded()) {
+            tvStatus.setText("STATUS: model is not loaded");
+        } else {
+            tvStatus.setText("STATUS: run model ...... ");
+            predictor.setInputImage(image);
+            runModel();
+        }
+    }
+
+    public void btn_choice_img_click(View view) {
+        if (requestAllPermissions()) {
+            openGallery();
+        }
+    }
+
+    public void btn_take_photo_click(View view) {
+        if (requestAllPermissions()) {
+            takePhoto();
+        }
+    }
+
+    @Override
+    protected void onDestroy() {
+        if (predictor != null) {
+            predictor.releaseModel();
+        }
+        worker.quit();
+        super.onDestroy();
+    }
+
+    public int get_run_mode() {
+        String run_mode = spRunMode.getSelectedItem().toString();
+        int mode;
+        switch (run_mode) {
+            case "检测+分类+识别":
+                mode = 1;
+                break;
+            case "检测+识别":
+                mode = 2;
+                break;
+            case "识别+分类":
+                mode = 3;
+                break;
+            case "检测":
+                mode = 4;
+                break;
+            case "识别":
+                mode = 5;
+                break;
+            case "分类":
+                mode = 6;
+                break;
+            default:
+                mode = 1;
+        }
+        return mode;
+    }
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java

@@ -0,0 +1,105 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.graphics.Bitmap;
+import android.util.Log;
+
+import java.util.ArrayList;
+import java.util.concurrent.atomic.AtomicBoolean;
+
+public class OCRPredictorNative {
+
+    private static final AtomicBoolean isSOLoaded = new AtomicBoolean();
+
+    public static void loadLibrary() throws RuntimeException {
+        if (!isSOLoaded.get() && isSOLoaded.compareAndSet(false, true)) {
+            try {
+                System.loadLibrary("Native");
+            } catch (Throwable e) {
+                RuntimeException exception = new RuntimeException(
+                        "Load libNative.so failed, please check it exists in apk file.", e);
+                throw exception;
+            }
+        }
+    }
+
+    private Config config;
+
+    private long nativePointer = 0;
+
+    public OCRPredictorNative(Config config) {
+        this.config = config;
+        loadLibrary();
+        nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename, config.useOpencl,
+                config.cpuThreadNum, config.cpuPower);
+        Log.i("OCRPredictorNative", "load success " + nativePointer);
+
+    }
+
+
+    public ArrayList<OcrResultModel> runImage(Bitmap originalImage, int max_size_len, int run_det, int run_cls, int run_rec) {
+        Log.i("OCRPredictorNative", "begin to run image ");
+        float[] rawResults = forward(nativePointer, originalImage, max_size_len, run_det, run_cls, run_rec);
+        ArrayList<OcrResultModel> results = postprocess(rawResults);
+        return results;
+    }
+
+    public static class Config {
+        public int useOpencl;
+        public int cpuThreadNum;
+        public String cpuPower;
+        public String detModelFilename;
+        public String recModelFilename;
+        public String clsModelFilename;
+
+    }
+
+    public void destroy() {
+        if (nativePointer != 0) {
+            release(nativePointer);
+            nativePointer = 0;
+        }
+    }
+
+    protected native long init(String detModelPath, String recModelPath, String clsModelPath, int useOpencl, int threadNum, String cpuMode);
+
+    protected native float[] forward(long pointer, Bitmap originalImage,int max_size_len, int run_det, int run_cls, int run_rec);
+
+    protected native void release(long pointer);
+
+    private ArrayList<OcrResultModel> postprocess(float[] raw) {
+        ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();
+        int begin = 0;
+
+        while (begin < raw.length) {
+            int point_num = Math.round(raw[begin]);
+            int word_num = Math.round(raw[begin + 1]);
+            OcrResultModel res = parse(raw, begin + 2, point_num, word_num);
+            begin += 2 + 1 + point_num * 2 + word_num + 2;
+            results.add(res);
+        }
+
+        return results;
+    }
+
+    private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
+        int current = begin;
+        OcrResultModel res = new OcrResultModel();
+        res.setConfidence(raw[current]);
+        current++;
+        for (int i = 0; i < pointNum; i++) {
+            res.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
+        }
+        current += (pointNum * 2);
+        for (int i = 0; i < wordNum; i++) {
+            int index = Math.round(raw[current + i]);
+            res.addWordIndex(index);
+        }
+        current += wordNum;
+        res.setClsIdx(raw[current]);
+        res.setClsConfidence(raw[current + 1]);
+        Log.i("OCRPredictorNative", "word finished " + wordNum);
+        return res;
+    }
+
+
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OcrResultModel.java

@@ -0,0 +1,79 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.graphics.Point;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class OcrResultModel {
+    private List<Point> points;
+    private List<Integer> wordIndex;
+    private String label;
+    private float confidence;
+    private float cls_idx;
+    private String cls_label;
+    private float cls_confidence;
+
+    public OcrResultModel() {
+        super();
+        points = new ArrayList<>();
+        wordIndex = new ArrayList<>();
+    }
+
+    public void addPoints(int x, int y) {
+        Point point = new Point(x, y);
+        points.add(point);
+    }
+
+    public void addWordIndex(int index) {
+        wordIndex.add(index);
+    }
+
+    public List<Point> getPoints() {
+        return points;
+    }
+
+    public List<Integer> getWordIndex() {
+        return wordIndex;
+    }
+
+    public String getLabel() {
+        return label;
+    }
+
+    public void setLabel(String label) {
+        this.label = label;
+    }
+
+    public float getConfidence() {
+        return confidence;
+    }
+
+    public void setConfidence(float confidence) {
+        this.confidence = confidence;
+    }
+
+    public float getClsIdx() {
+        return cls_idx;
+    }
+
+    public void setClsIdx(float idx) {
+        this.cls_idx = idx;
+    }
+
+    public String getClsLabel() {
+        return cls_label;
+    }
+
+    public void setClsLabel(String label) {
+        this.cls_label = label;
+    }
+
+    public float getClsConfidence() {
+        return cls_confidence;
+    }
+
+    public void setClsConfidence(float confidence) {
+        this.cls_confidence = confidence;
+    }
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java

@@ -0,0 +1,278 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.Canvas;
+import android.graphics.Color;
+import android.graphics.Paint;
+import android.graphics.Path;
+import android.graphics.Point;
+import android.util.Log;
+
+import java.io.File;
+import java.io.InputStream;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.List;
+import java.util.Vector;
+
+import static android.graphics.Color.*;
+
+public class Predictor {
+    private static final String TAG = Predictor.class.getSimpleName();
+    public boolean isLoaded = false;
+    public int warmupIterNum = 1;
+    public int inferIterNum = 1;
+    public int cpuThreadNum = 4;
+    public String cpuPowerMode = "LITE_POWER_HIGH";
+    public String modelPath = "";
+    public String modelName = "";
+    protected OCRPredictorNative paddlePredictor = null;
+    protected float inferenceTime = 0;
+    // Only for object detection
+    protected Vector<String> wordLabels = new Vector<String>();
+    protected int detLongSize = 960;
+    protected float scoreThreshold = 0.1f;
+    protected Bitmap inputImage = null;
+    protected Bitmap outputImage = null;
+    protected volatile String outputResult = "";
+    protected float postprocessTime = 0;
+
+
+    public Predictor() {
+    }
+
+    public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
+        isLoaded = loadModel(appCtx, modelPath, useOpencl, cpuThreadNum, cpuPowerMode);
+        if (!isLoaded) {
+            return false;
+        }
+        isLoaded = loadLabel(appCtx, labelPath);
+        return isLoaded;
+    }
+
+
+    public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode,
+                        int detLongSize, float scoreThreshold) {
+        boolean isLoaded = init(appCtx, modelPath, labelPath, useOpencl, cpuThreadNum, cpuPowerMode);
+        if (!isLoaded) {
+            return false;
+        }
+        this.detLongSize = detLongSize;
+        this.scoreThreshold = scoreThreshold;
+        return true;
+    }
+
+    protected boolean loadModel(Context appCtx, String modelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
+        // Release model if exists
+        releaseModel();
+
+        // Load model
+        if (modelPath.isEmpty()) {
+            return false;
+        }
+        String realPath = modelPath;
+        if (!modelPath.substring(0, 1).equals("/")) {
+            // Read model files from custom path if the first character of mode path is '/'
+            // otherwise copy model to cache from assets
+            realPath = appCtx.getCacheDir() + "/" + modelPath;
+            Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
+        }
+        if (realPath.isEmpty()) {
+            return false;
+        }
+
+        OCRPredictorNative.Config config = new OCRPredictorNative.Config();
+        config.useOpencl = useOpencl;
+        config.cpuThreadNum = cpuThreadNum;
+        config.cpuPower = cpuPowerMode;
+        config.detModelFilename = realPath + File.separator + "det_db.nb";
+        config.recModelFilename = realPath + File.separator + "rec_crnn.nb";
+        config.clsModelFilename = realPath + File.separator + "cls.nb";
+        Log.i("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
+        paddlePredictor = new OCRPredictorNative(config);
+
+        this.cpuThreadNum = cpuThreadNum;
+        this.cpuPowerMode = cpuPowerMode;
+        this.modelPath = realPath;
+        this.modelName = realPath.substring(realPath.lastIndexOf("/") + 1);
+        return true;
+    }
+
+    public void releaseModel() {
+        if (paddlePredictor != null) {
+            paddlePredictor.destroy();
+            paddlePredictor = null;
+        }
+        isLoaded = false;
+        cpuThreadNum = 1;
+        cpuPowerMode = "LITE_POWER_HIGH";
+        modelPath = "";
+        modelName = "";
+    }
+
+    protected boolean loadLabel(Context appCtx, String labelPath) {
+        wordLabels.clear();
+        wordLabels.add("black");
+        // Load word labels from file
+        try {
+            InputStream assetsInputStream = appCtx.getAssets().open(labelPath);
+            int available = assetsInputStream.available();
+            byte[] lines = new byte[available];
+            assetsInputStream.read(lines);
+            assetsInputStream.close();
+            String words = new String(lines);
+            String[] contents = words.split("\n");
+            for (String content : contents) {
+                wordLabels.add(content);
+            }
+            wordLabels.add(" ");
+            Log.i(TAG, "Word label size: " + wordLabels.size());
+        } catch (Exception e) {
+            Log.e(TAG, e.getMessage());
+            return false;
+        }
+        return true;
+    }
+
+
+    public boolean runModel(int run_det, int run_cls, int run_rec) {
+        if (inputImage == null || !isLoaded()) {
+            return false;
+        }
+
+        // Warm up
+        for (int i = 0; i < warmupIterNum; i++) {
+            paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
+        }
+        warmupIterNum = 0; // do not need warm
+        // Run inference
+        Date start = new Date();
+        ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
+        Date end = new Date();
+        inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
+
+        results = postprocess(results);
+        Log.i(TAG, "[stat] Inference Time: " + inferenceTime + " ;Box Size " + results.size());
+        drawResults(results);
+
+        return true;
+    }
+
+    public boolean isLoaded() {
+        return paddlePredictor != null && isLoaded;
+    }
+
+    public String modelPath() {
+        return modelPath;
+    }
+
+    public String modelName() {
+        return modelName;
+    }
+
+    public int cpuThreadNum() {
+        return cpuThreadNum;
+    }
+
+    public String cpuPowerMode() {
+        return cpuPowerMode;
+    }
+
+    public float inferenceTime() {
+        return inferenceTime;
+    }
+
+    public Bitmap inputImage() {
+        return inputImage;
+    }
+
+    public Bitmap outputImage() {
+        return outputImage;
+    }
+
+    public String outputResult() {
+        return outputResult;
+    }
+
+    public float postprocessTime() {
+        return postprocessTime;
+    }
+
+
+    public void setInputImage(Bitmap image) {
+        if (image == null) {
+            return;
+        }
+        this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
+    }
+
+    private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
+        for (OcrResultModel r : results) {
+            StringBuffer word = new StringBuffer();
+            for (int index : r.getWordIndex()) {
+                if (index >= 0 && index < wordLabels.size()) {
+                    word.append(wordLabels.get(index));
+                } else {
+                    Log.e(TAG, "Word index is not in label list:" + index);
+                    word.append("×");
+                }
+            }
+            r.setLabel(word.toString());
+            r.setClsLabel(r.getClsIdx() == 1 ? "180" : "0");
+        }
+        return results;
+    }
+
+    private void drawResults(ArrayList<OcrResultModel> results) {
+        StringBuffer outputResultSb = new StringBuffer("");
+        for (int i = 0; i < results.size(); i++) {
+            OcrResultModel result = results.get(i);
+            StringBuilder sb = new StringBuilder("");
+            if(result.getPoints().size()>0){
+                sb.append("Det: ");
+                for (Point p : result.getPoints()) {
+                    sb.append("(").append(p.x).append(",").append(p.y).append(") ");
+                }
+            }
+            if(result.getLabel().length() > 0){
+                sb.append("\n Rec: ").append(result.getLabel());
+                sb.append(",").append(result.getConfidence());
+            }
+            if(result.getClsIdx()!=-1){
+                sb.append(" Cls: ").append(result.getClsLabel());
+                sb.append(",").append(result.getClsConfidence());
+            }
+            Log.i(TAG, sb.toString()); // show LOG in Logcat panel
+            outputResultSb.append(i + 1).append(": ").append(sb.toString()).append("\n");
+        }
+        outputResult = outputResultSb.toString();
+        outputImage = inputImage;
+        Canvas canvas = new Canvas(outputImage);
+        Paint paintFillAlpha = new Paint();
+        paintFillAlpha.setStyle(Paint.Style.FILL);
+        paintFillAlpha.setColor(Color.parseColor("#3B85F5"));
+        paintFillAlpha.setAlpha(50);
+
+        Paint paint = new Paint();
+        paint.setColor(Color.parseColor("#3B85F5"));
+        paint.setStrokeWidth(5);
+        paint.setStyle(Paint.Style.STROKE);
+
+        for (OcrResultModel result : results) {
+            Path path = new Path();
+            List<Point> points = result.getPoints();
+            if(points.size()==0){
+                continue;
+            }
+            path.moveTo(points.get(0).x, points.get(0).y);
+            for (int i = points.size() - 1; i >= 0; i--) {
+                Point p = points.get(i);
+                path.lineTo(p.x, p.y);
+            }
+            canvas.drawPath(path, paint);
+            canvas.drawPath(path, paintFillAlpha);
+        }
+    }
+
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/SettingsActivity.java

@@ -0,0 +1,172 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.CheckBoxPreference;
+import android.preference.EditTextPreference;
+import android.preference.ListPreference;
+
+import androidx.appcompat.app.ActionBar;
+
+import java.util.ArrayList;
+import java.util.List;
+
+
+public class SettingsActivity extends AppCompatPreferenceActivity implements SharedPreferences.OnSharedPreferenceChangeListener {
+    ListPreference lpChoosePreInstalledModel = null;
+    CheckBoxPreference cbEnableCustomSettings = null;
+    EditTextPreference etModelPath = null;
+    EditTextPreference etLabelPath = null;
+    ListPreference etImagePath = null;
+    ListPreference lpCPUThreadNum = null;
+    ListPreference lpCPUPowerMode = null;
+    EditTextPreference etDetLongSize = null;
+    EditTextPreference etScoreThreshold = null;
+
+    List<String> preInstalledModelPaths = null;
+    List<String> preInstalledLabelPaths = null;
+    List<String> preInstalledImagePaths = null;
+    List<String> preInstalledDetLongSizes = null;
+    List<String> preInstalledCPUThreadNums = null;
+    List<String> preInstalledCPUPowerModes = null;
+    List<String> preInstalledInputColorFormats = null;
+    List<String> preInstalledInputMeans = null;
+    List<String> preInstalledInputStds = null;
+    List<String> preInstalledScoreThresholds = null;
+
+    @Override
+    public void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        addPreferencesFromResource(R.xml.settings);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        // Initialized pre-installed models
+        preInstalledModelPaths = new ArrayList<String>();
+        preInstalledLabelPaths = new ArrayList<String>();
+        preInstalledImagePaths = new ArrayList<String>();
+        preInstalledDetLongSizes = new ArrayList<String>();
+        preInstalledCPUThreadNums = new ArrayList<String>();
+        preInstalledCPUPowerModes = new ArrayList<String>();
+        preInstalledInputColorFormats = new ArrayList<String>();
+        preInstalledInputMeans = new ArrayList<String>();
+        preInstalledInputStds = new ArrayList<String>();
+        preInstalledScoreThresholds = new ArrayList<String>();
+        // Add ssd_mobilenet_v1_pascalvoc_for_cpu
+        preInstalledModelPaths.add(getString(R.string.MODEL_PATH_DEFAULT));
+        preInstalledLabelPaths.add(getString(R.string.LABEL_PATH_DEFAULT));
+        preInstalledImagePaths.add(getString(R.string.IMAGE_PATH_DEFAULT));
+        preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
+        preInstalledDetLongSizes.add(getString(R.string.DET_LONG_SIZE_DEFAULT));
+        preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
+
+        // Setup UI components
+        lpChoosePreInstalledModel =
+                (ListPreference) findPreference(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY));
+        String[] preInstalledModelNames = new String[preInstalledModelPaths.size()];
+        for (int i = 0; i < preInstalledModelPaths.size(); i++) {
+            preInstalledModelNames[i] =
+                    preInstalledModelPaths.get(i).substring(preInstalledModelPaths.get(i).lastIndexOf("/") + 1);
+        }
+        lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
+        lpChoosePreInstalledModel.setEntryValues(preInstalledModelPaths.toArray(new String[preInstalledModelPaths.size()]));
+        cbEnableCustomSettings =
+                (CheckBoxPreference) findPreference(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY));
+        etModelPath = (EditTextPreference) findPreference(getString(R.string.MODEL_PATH_KEY));
+        etModelPath.setTitle("Model Path (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etLabelPath = (EditTextPreference) findPreference(getString(R.string.LABEL_PATH_KEY));
+        etImagePath = (ListPreference) findPreference(getString(R.string.IMAGE_PATH_KEY));
+        lpCPUThreadNum =
+                (ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
+        lpCPUPowerMode =
+                (ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
+        etDetLongSize = (EditTextPreference) findPreference(getString(R.string.DET_LONG_SIZE_KEY));
+        etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
+    }
+
+    private void reloadPreferenceAndUpdateUI() {
+        SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
+        boolean enableCustomSettings =
+                sharedPreferences.getBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+        String modelPath = sharedPreferences.getString(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        int modelIdx = lpChoosePreInstalledModel.findIndexOfValue(modelPath);
+        if (modelIdx >= 0 && modelIdx < preInstalledModelPaths.size()) {
+            if (!enableCustomSettings) {
+                SharedPreferences.Editor editor = sharedPreferences.edit();
+                editor.putString(getString(R.string.MODEL_PATH_KEY), preInstalledModelPaths.get(modelIdx));
+                editor.putString(getString(R.string.LABEL_PATH_KEY), preInstalledLabelPaths.get(modelIdx));
+                editor.putString(getString(R.string.IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
+                editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
+                editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
+                editor.putString(getString(R.string.DET_LONG_SIZE_KEY), preInstalledDetLongSizes.get(modelIdx));
+                editor.putString(getString(R.string.SCORE_THRESHOLD_KEY),
+                        preInstalledScoreThresholds.get(modelIdx));
+                editor.apply();
+            }
+            lpChoosePreInstalledModel.setSummary(modelPath);
+        }
+        cbEnableCustomSettings.setChecked(enableCustomSettings);
+        etModelPath.setEnabled(enableCustomSettings);
+        etLabelPath.setEnabled(enableCustomSettings);
+        etImagePath.setEnabled(enableCustomSettings);
+        lpCPUThreadNum.setEnabled(enableCustomSettings);
+        lpCPUPowerMode.setEnabled(enableCustomSettings);
+        etDetLongSize.setEnabled(enableCustomSettings);
+        etScoreThreshold.setEnabled(enableCustomSettings);
+        modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        String labelPath = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
+                getString(R.string.LABEL_PATH_DEFAULT));
+        String imagePath = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
+                getString(R.string.IMAGE_PATH_DEFAULT));
+        String cpuThreadNum = sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
+                getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        String cpuPowerMode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
+                getString(R.string.CPU_POWER_MODE_DEFAULT));
+        String detLongSize = sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
+                getString(R.string.DET_LONG_SIZE_DEFAULT));
+        String scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
+                getString(R.string.SCORE_THRESHOLD_DEFAULT));
+        etModelPath.setSummary(modelPath);
+        etModelPath.setText(modelPath);
+        etLabelPath.setSummary(labelPath);
+        etLabelPath.setText(labelPath);
+        etImagePath.setSummary(imagePath);
+        etImagePath.setValue(imagePath);
+        lpCPUThreadNum.setValue(cpuThreadNum);
+        lpCPUThreadNum.setSummary(cpuThreadNum);
+        lpCPUPowerMode.setValue(cpuPowerMode);
+        lpCPUPowerMode.setSummary(cpuPowerMode);
+        etDetLongSize.setSummary(detLongSize);
+        etDetLongSize.setText(detLongSize);
+        etScoreThreshold.setText(scoreThreshold);
+        etScoreThreshold.setSummary(scoreThreshold);
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
+        reloadPreferenceAndUpdateUI();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
+    }
+
+    @Override
+    public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
+        if (key.equals(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY))) {
+            SharedPreferences.Editor editor = sharedPreferences.edit();
+            editor.putBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+            editor.commit();
+        }
+        reloadPreferenceAndUpdateUI();
+    }
+}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Utils.java

@@ -0,0 +1,159 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.Matrix;
+import android.media.ExifInterface;
+import android.os.Environment;
+
+import java.io.*;
+
+public class Utils {
+    private static final String TAG = Utils.class.getSimpleName();
+
+    public static void copyFileFromAssets(Context appCtx, String srcPath, String dstPath) {
+        if (srcPath.isEmpty() || dstPath.isEmpty()) {
+            return;
+        }
+        InputStream is = null;
+        OutputStream os = null;
+        try {
+            is = new BufferedInputStream(appCtx.getAssets().open(srcPath));
+            os = new BufferedOutputStream(new FileOutputStream(new File(dstPath)));
+            byte[] buffer = new byte[1024];
+            int length = 0;
+            while ((length = is.read(buffer)) != -1) {
+                os.write(buffer, 0, length);
+            }
+        } catch (FileNotFoundException e) {
+            e.printStackTrace();
+        } catch (IOException e) {
+            e.printStackTrace();
+        } finally {
+            try {
+                os.close();
+                is.close();
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+    }
+
+    public static void copyDirectoryFromAssets(Context appCtx, String srcDir, String dstDir) {
+        if (srcDir.isEmpty() || dstDir.isEmpty()) {
+            return;
+        }
+        try {
+            if (!new File(dstDir).exists()) {
+                new File(dstDir).mkdirs();
+            }
+            for (String fileName : appCtx.getAssets().list(srcDir)) {
+                String srcSubPath = srcDir + File.separator + fileName;
+                String dstSubPath = dstDir + File.separator + fileName;
+                if (new File(srcSubPath).isDirectory()) {
+                    copyDirectoryFromAssets(appCtx, srcSubPath, dstSubPath);
+                } else {
+                    copyFileFromAssets(appCtx, srcSubPath, dstSubPath);
+                }
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public static float[] parseFloatsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        float[] floats = new float[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            floats[i] = Float.parseFloat(pieces[i].trim());
+        }
+        return floats;
+    }
+
+    public static long[] parseLongsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        long[] longs = new long[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            longs[i] = Long.parseLong(pieces[i].trim());
+        }
+        return longs;
+    }
+
+    public static String getSDCardDirectory() {
+        return Environment.getExternalStorageDirectory().getAbsolutePath();
+    }
+
+    public static boolean isSupportedNPU() {
+        return false;
+        // String hardware = android.os.Build.HARDWARE;
+        // return hardware.equalsIgnoreCase("kirin810") || hardware.equalsIgnoreCase("kirin990");
+    }
+
+    public static Bitmap resizeWithStep(Bitmap bitmap, int maxLength, int step) {
+        int width = bitmap.getWidth();
+        int height = bitmap.getHeight();
+        int maxWH = Math.max(width, height);
+        float ratio = 1;
+        int newWidth = width;
+        int newHeight = height;
+        if (maxWH > maxLength) {
+            ratio = maxLength * 1.0f / maxWH;
+            newWidth = (int) Math.floor(ratio * width);
+            newHeight = (int) Math.floor(ratio * height);
+        }
+
+        newWidth = newWidth - newWidth % step;
+        if (newWidth == 0) {
+            newWidth = step;
+        }
+        newHeight = newHeight - newHeight % step;
+        if (newHeight == 0) {
+            newHeight = step;
+        }
+        return Bitmap.createScaledBitmap(bitmap, newWidth, newHeight, true);
+    }
+
+    public static Bitmap rotateBitmap(Bitmap bitmap, int orientation) {
+
+        Matrix matrix = new Matrix();
+        switch (orientation) {
+            case ExifInterface.ORIENTATION_NORMAL:
+                return bitmap;
+            case ExifInterface.ORIENTATION_FLIP_HORIZONTAL:
+                matrix.setScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_180:
+                matrix.setRotate(180);
+                break;
+            case ExifInterface.ORIENTATION_FLIP_VERTICAL:
+                matrix.setRotate(180);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_TRANSPOSE:
+                matrix.setRotate(90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_90:
+                matrix.setRotate(90);
+                break;
+            case ExifInterface.ORIENTATION_TRANSVERSE:
+                matrix.setRotate(-90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_270:
+                matrix.setRotate(-90);
+                break;
+            default:
+                return bitmap;
+        }
+        try {
+            Bitmap bmRotated = Bitmap.createBitmap(bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), matrix, true);
+            bitmap.recycle();
+            return bmRotated;
+        }
+        catch (OutOfMemoryError e) {
+            e.printStackTrace();
+            return null;
+        }
+    }
+}

deploy/android_demo/app/src/main/res/drawable-v24/ic_launcher_foreground.xml

@@ -0,0 +1,34 @@
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillType="evenOdd"
+        android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="78.5885"
+                android:endY="90.9159"
+                android:startX="48.7653"
+                android:startY="61.0927"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>

deploy/android_demo/app/src/main/res/drawable/ic_launcher_background.xml

@@ -0,0 +1,170 @@
+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#008577"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,0L49,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,0L59,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,0L69,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,0L79,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M89,0L89,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,69L108,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,79L108,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>

deploy/android_demo/app/src/main/res/layout/activity_main.xml

@@ -0,0 +1,180 @@
+<?xml version="1.0" encoding="utf-8"?>
+<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+                                             xmlns:app="http://schemas.android.com/apk/res-auto"
+                                             xmlns:tools="http://schemas.android.com/tools"
+                                             android:layout_width="match_parent"
+                                             android:layout_height="match_parent"
+                                             tools:context=".MainActivity">
+
+    <RelativeLayout
+            android:layout_width="match_parent"
+            android:layout_height="match_parent">
+
+        <LinearLayout
+                android:id="@+id/v_input_info"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:layout_alignParentTop="true"
+                android:orientation="vertical">
+
+            <LinearLayout
+                android:id="@+id/btn_layout"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:orientation="horizontal">
+
+
+                <Button
+                    android:id="@+id/btn_run_model"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_run_model_click"
+                    android:text="运行模型" />
+                <Button
+                    android:id="@+id/btn_take_photo"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_take_photo_click"
+                    android:text="拍照识别" />
+                <Button
+                    android:id="@+id/btn_choice_img"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_choice_img_click"
+                    android:text="选取图片" />
+
+                <Button
+                    android:id="@+id/btn_reset_img"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_reset_img_click"
+                    android:text="清空绘图" />
+
+            </LinearLayout>
+            <LinearLayout
+                android:id="@+id/run_mode_layout"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:orientation="horizontal">
+
+                <CheckBox
+                    android:id="@+id/cb_opencl"
+                    android:layout_width="0dp"
+                    android:layout_weight="1"
+                    android:layout_height="wrap_content"
+                    android:text="开启OPENCL"
+                    android:onClick="cb_opencl_click"
+                    android:visibility="gone"/>
+                <TextView
+                    android:layout_width="0dp"
+                    android:layout_weight="0.5"
+                    android:layout_height="wrap_content"
+                    android:text="运行模式:"/>
+
+                <Spinner
+                    android:id="@+id/sp_run_mode"
+                    android:layout_width="0dp"
+                    android:layout_weight="1.5"
+                    android:layout_height="wrap_content"
+                    android:entries="@array/run_Model"
+                    />
+
+            </LinearLayout>
+
+            <TextView
+                    android:id="@+id/tv_input_setting"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:lineSpacingExtra="4dp"
+                    android:singleLine="false"
+                    android:maxLines="6"
+                    android:text=""/>
+            <TextView
+                android:id="@+id/tv_model_img_status"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:scrollbars="vertical"
+                android:layout_marginLeft="12dp"
+                android:layout_marginRight="12dp"
+                android:layout_marginTop="-5dp"
+                android:layout_marginBottom="5dp"
+                android:lineSpacingExtra="4dp"
+                android:singleLine="false"
+                android:maxLines="6"
+                android:text="STATUS: ok"/>
+
+        </LinearLayout>
+
+        <RelativeLayout
+                android:layout_width="match_parent"
+                android:layout_height="match_parent"
+                android:layout_above="@+id/v_output_info"
+                android:layout_below="@+id/v_input_info">
+
+            <ImageView
+                    android:id="@+id/iv_input_image"
+                    android:layout_width="400dp"
+                    android:layout_height="400dp"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:adjustViewBounds="true"
+                    android:scaleType="fitCenter"/>
+        </RelativeLayout>
+
+
+        <RelativeLayout
+                android:id="@+id/v_output_info"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_alignParentBottom="true"
+                android:layout_centerHorizontal="true">
+
+            <TextView
+                    android:id="@+id/tv_output_result"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_alignParentTop="true"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:textAlignment="center"
+                    android:lineSpacingExtra="5dp"
+                    android:singleLine="false"
+                    android:maxLines="5"
+                    android:text=""/>
+
+            <TextView
+                    android:id="@+id/tv_inference_time"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_below="@+id/tv_output_result"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:textAlignment="center"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="10dp"
+                    android:text=""/>
+
+        </RelativeLayout>
+
+    </RelativeLayout>
+</androidx.constraintlayout.widget.ConstraintLayout>

deploy/android_demo/app/src/main/res/menu/menu_action_options.xml

@@ -0,0 +1,10 @@
+<menu xmlns:android="http://schemas.android.com/apk/res/android"
+      xmlns:app="http://schemas.android.com/apk/res-auto">
+
+    <group>
+        <item
+                android:id="@+id/settings"
+                android:title="Settings..."
+                app:showAsAction="withText"/>
+    </group>
+</menu>

deploy/android_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>

deploy/android_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>

deploy/android_demo/app/src/main/res/values/arrays.xml

@@ -0,0 +1,67 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <string-array name="image_name_entries">
+        <item>det_0.jpg</item>
+        <item>det_90.jpg</item>
+        <item>det_180.jpg</item>
+        <item>det_270.jpg</item>
+        <item>rec_0.jpg</item>
+        <item>rec_0_180.jpg</item>
+        <item>rec_1.jpg</item>
+        <item>rec_1_180.jpg</item>
+    </string-array>
+    <string-array name="image_name_values">
+        <item>images/det_0.jpg</item>
+        <item>images/det_90.jpg</item>
+        <item>images/det_180.jpg</item>
+        <item>images/det_270.jpg</item>
+        <item>images/rec_0.jpg</item>
+        <item>images/rec_0_180.jpg</item>
+        <item>images/rec_1.jpg</item>
+        <item>images/rec_1_180.jpg</item>
+    </string-array>
+    <string-array name="cpu_thread_num_entries">
+        <item>1 threads</item>
+        <item>2 threads</item>
+        <item>4 threads</item>
+        <item>8 threads</item>
+    </string-array>
+    <string-array name="cpu_thread_num_values">
+        <item>1</item>
+        <item>2</item>
+        <item>4</item>
+        <item>8</item>
+    </string-array>
+    <string-array name="cpu_power_mode_entries">
+        <item>HIGH(only big cores)</item>
+        <item>LOW(only LITTLE cores)</item>
+        <item>FULL(all cores)</item>
+        <item>NO_BIND(depends on system)</item>
+        <item>RAND_HIGH</item>
+        <item>RAND_LOW</item>
+    </string-array>
+    <string-array name="cpu_power_mode_values">
+        <item>LITE_POWER_HIGH</item>
+        <item>LITE_POWER_LOW</item>
+        <item>LITE_POWER_FULL</item>
+        <item>LITE_POWER_NO_BIND</item>
+        <item>LITE_POWER_RAND_HIGH</item>
+        <item>LITE_POWER_RAND_LOW</item>
+    </string-array>
+    <string-array name="input_color_format_entries">
+        <item>BGR color format</item>
+        <item>RGB color format</item>
+    </string-array>
+    <string-array name="input_color_format_values">
+        <item>BGR</item>
+        <item>RGB</item>
+    </string-array>
+    <string-array name="run_Model">
+        <item>检测+分类+识别</item>
+        <item>检测+识别</item>
+        <item>分类+识别</item>
+        <item>检测</item>
+        <item>识别</item>
+        <item>分类</item>
+    </string-array>
+</resources>

deploy/android_demo/app/src/main/res/values/colors.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="colorPrimary">#008577</color>
+    <color name="colorPrimaryDark">#00574B</color>
+    <color name="colorAccent">#D81B60</color>
+</resources>

deploy/android_demo/app/src/main/res/values/strings.xml

@@ -0,0 +1,19 @@
+<resources>
+    <string name="app_name">PaddleOCR</string>
+    <string name="CHOOSE_PRE_INSTALLED_MODEL_KEY">CHOOSE_PRE_INSTALLED_MODEL_KEY</string>
+    <string name="ENABLE_CUSTOM_SETTINGS_KEY">ENABLE_CUSTOM_SETTINGS_KEY</string>
+    <string name="MODEL_PATH_KEY">MODEL_PATH_KEY</string>
+    <string name="LABEL_PATH_KEY">LABEL_PATH_KEY</string>
+    <string name="IMAGE_PATH_KEY">IMAGE_PATH_KEY</string>
+    <string name="CPU_THREAD_NUM_KEY">CPU_THREAD_NUM_KEY</string>
+    <string name="CPU_POWER_MODE_KEY">CPU_POWER_MODE_KEY</string>
+    <string name="DET_LONG_SIZE_KEY">DET_LONG_SIZE_KEY</string>
+    <string name="SCORE_THRESHOLD_KEY">SCORE_THRESHOLD_KEY</string>
+    <string name="MODEL_PATH_DEFAULT">models/ch_PP-OCRv2</string>
+    <string name="LABEL_PATH_DEFAULT">labels/ppocr_keys_v1.txt</string>
+    <string name="IMAGE_PATH_DEFAULT">images/det_0.jpg</string>
+    <string name="CPU_THREAD_NUM_DEFAULT">4</string>
+    <string name="CPU_POWER_MODE_DEFAULT">LITE_POWER_HIGH</string>
+    <string name="DET_LONG_SIZE_DEFAULT">960</string>
+    <string name="SCORE_THRESHOLD_DEFAULT">0.1</string>
+</resources>

deploy/android_demo/app/src/main/res/values/styles.xml

@@ -0,0 +1,25 @@
+<resources>
+
+    <!-- Base application theme. -->
+    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
+        <!-- Customize your theme here. -->
+        <item name="colorPrimary">@color/colorPrimary</item>
+        <item name="colorPrimaryDark">@color/colorPrimaryDark</item>
+        <item name="colorAccent">@color/colorAccent</item>
+        <item name="actionOverflowMenuStyle">@style/OverflowMenuStyle</item>
+    </style>
+
+    <style name="OverflowMenuStyle" parent="Widget.AppCompat.Light.PopupMenu.Overflow">
+        <item name="overlapAnchor">false</item>
+    </style>
+
+    <style name="AppTheme.NoActionBar">
+        <item name="windowActionBar">false</item>
+        <item name="windowNoTitle">true</item>
+    </style>
+
+    <style name="AppTheme.AppBarOverlay" parent="ThemeOverlay.AppCompat.Dark.ActionBar"/>
+
+    <style name="AppTheme.PopupOverlay" parent="ThemeOverlay.AppCompat.Light"/>
+
+</resources>

deploy/android_demo/app/src/main/res/xml/file_paths.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="utf-8"?>
+<paths xmlns:android="http://schemas.android.com/apk/res/android">
+    <external-files-path name="my_images" path="Pictures" />
+</paths>

deploy/android_demo/app/src/main/res/xml/settings.xml

@@ -0,0 +1,61 @@
+<?xml version="1.0" encoding="utf-8"?>
+<PreferenceScreen xmlns:android="http://schemas.android.com/apk/res/android" >
+    <PreferenceCategory android:title="Model Settings">
+        <ListPreference
+                android:defaultValue="@string/MODEL_PATH_DEFAULT"
+                android:key="@string/CHOOSE_PRE_INSTALLED_MODEL_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="Choose pre-installed models" />
+        <CheckBoxPreference
+                android:defaultValue="false"
+                android:key="@string/ENABLE_CUSTOM_SETTINGS_KEY"
+                android:summaryOn="Enable"
+                android:summaryOff="Disable"
+                android:title="Enable custom settings"/>
+        <EditTextPreference
+                android:key="@string/MODEL_PATH_KEY"
+                android:defaultValue="@string/MODEL_PATH_DEFAULT"
+                android:title="Model Path" />
+        <EditTextPreference
+                android:key="@string/LABEL_PATH_KEY"
+                android:defaultValue="@string/LABEL_PATH_DEFAULT"
+                android:title="Label Path" />
+        <ListPreference
+                android:key="@string/IMAGE_PATH_KEY"
+                android:defaultValue="@string/IMAGE_PATH_DEFAULT"
+                android:entries="@array/image_name_entries"
+                android:entryValues="@array/image_name_values"
+                android:title="Image Path" />
+    </PreferenceCategory>
+    <PreferenceCategory android:title="CPU Settings">
+        <ListPreference
+                android:defaultValue="@string/CPU_THREAD_NUM_DEFAULT"
+                android:key="@string/CPU_THREAD_NUM_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Thread Num"
+                android:entries="@array/cpu_thread_num_entries"
+                android:entryValues="@array/cpu_thread_num_values"/>
+        <ListPreference
+                android:defaultValue="@string/CPU_POWER_MODE_DEFAULT"
+                android:key="@string/CPU_POWER_MODE_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Power Mode"
+                android:entries="@array/cpu_power_mode_entries"
+                android:entryValues="@array/cpu_power_mode_values"/>
+    </PreferenceCategory>
+    <PreferenceCategory android:title="Input Settings">
+        <EditTextPreference
+                android:key="@string/DET_LONG_SIZE_KEY"
+                android:defaultValue="@string/DET_LONG_SIZE_DEFAULT"
+                android:title="det long size" />
+    </PreferenceCategory>
+    <PreferenceCategory android:title="Output Settings">
+        <EditTextPreference
+                android:key="@string/SCORE_THRESHOLD_KEY"
+                android:defaultValue="@string/SCORE_THRESHOLD_DEFAULT"
+                android:title="Score Threshold" />
+    </PreferenceCategory>
+</PreferenceScreen>

deploy/android_demo/app/src/test/java/com/baidu/paddle/lite/demo/ocr/ExampleUnitTest.java

@@ -0,0 +1,17 @@
+package com.baidu.paddle.lite.demo.ocr;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+public class ExampleUnitTest {
+    @Test
+    public void addition_isCorrect() {
+        assertEquals(4, 2 + 2);
+    }
+}

deploy/android_demo/build.gradle

@@ -0,0 +1,27 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:4.1.2'
+        
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

deploy/android_demo/gradle.properties

@@ -0,0 +1,15 @@
+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx1536m
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true
+
+android.useAndroidX=true

deploy/android_demo/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Thu Feb 04 20:28:08 CST 2021
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-6.5-bin.zip

deploy/android_demo/gradlew

@@ -0,0 +1,170 @@
+#!/usr/bin/env sh
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+    die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME\n\nPlease set the JAVA_HOME variable in your environment to match the location of your Java installation."
+    fi
+
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=$(save "$@")
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
+if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
+  cd "$(dirname "$0")"
+fi
+
+exec "$JAVACMD" "$@"

deploy/android_demo/gradlew.bat

@@ -0,0 +1,84 @@
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windows variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

deploy/android_demo/settings.gradle

@@ -0,0 +1 @@
+include ':app'

deploy/avh/.gitignore

@@ -0,0 +1,5 @@
+include/inputs.h
+include/outputs.h
+
+__pycache__/
+build/

deploy/avh/Makefile

@@ -0,0 +1,129 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Makefile to build demo
+
+# Setup build environment
+BUILD_DIR := build
+
+ARM_CPU = ARMCM55
+ETHOSU_PATH = /opt/arm/ethosu
+CMSIS_PATH ?= ${ETHOSU_PATH}/cmsis
+ETHOSU_PLATFORM_PATH ?= ${ETHOSU_PATH}/core_platform
+STANDALONE_CRT_PATH := $(abspath $(BUILD_DIR))/runtime
+CORSTONE_300_PATH = ${ETHOSU_PLATFORM_PATH}/targets/corstone-300
+PKG_COMPILE_OPTS = -g -Wall -O2 -Wno-incompatible-pointer-types -Wno-format -mcpu=cortex-m55 -mthumb -mfloat-abi=hard -std=gnu99
+CMAKE ?= cmake
+CC = arm-none-eabi-gcc
+AR = arm-none-eabi-ar
+RANLIB = arm-none-eabi-ranlib
+PKG_CFLAGS = ${PKG_COMPILE_OPTS} \
+	-I${STANDALONE_CRT_PATH}/include \
+	-I${STANDALONE_CRT_PATH}/src/runtime/crt/include \
+	-I${PWD}/include \
+	-I${CORSTONE_300_PATH} \
+	-I${CMSIS_PATH}/Device/ARM/${ARM_CPU}/Include/ \
+	-I${CMSIS_PATH}/CMSIS/Core/Include \
+	-I${CMSIS_PATH}/CMSIS/NN/Include \
+	-I${CMSIS_PATH}/CMSIS/DSP/Include \
+	-I$(abspath $(BUILD_DIR))/codegen/host/include
+CMSIS_NN_CMAKE_FLAGS = -DCMAKE_TOOLCHAIN_FILE=$(abspath $(BUILD_DIR))/../arm-none-eabi-gcc.cmake \
+	-DTARGET_CPU=cortex-m55 \
+	-DBUILD_CMSIS_NN_FUNCTIONS=YES
+PKG_LDFLAGS = -lm -specs=nosys.specs -static -T corstone300.ld
+
+$(ifeq VERBOSE,1)
+QUIET ?=
+$(else)
+QUIET ?= @
+$(endif)
+
+DEMO_MAIN = src/demo_bare_metal.c
+CODEGEN_SRCS = $(wildcard $(abspath $(BUILD_DIR))/codegen/host/src/*.c)
+CODEGEN_OBJS = $(subst .c,.o,$(CODEGEN_SRCS))
+CMSIS_STARTUP_SRCS = $(wildcard ${CMSIS_PATH}/Device/ARM/${ARM_CPU}/Source/*.c)
+UART_SRCS = $(wildcard ${CORSTONE_300_PATH}/*.c)
+
+demo: $(BUILD_DIR)/demo
+
+$(BUILD_DIR)/stack_allocator.o: $(STANDALONE_CRT_PATH)/src/runtime/crt/memory/stack_allocator.c
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)$(CC) -c $(PKG_CFLAGS) -o $@  $^
+
+$(BUILD_DIR)/crt_backend_api.o: $(STANDALONE_CRT_PATH)/src/runtime/crt/common/crt_backend_api.c
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)$(CC) -c $(PKG_CFLAGS) -o $@  $^
+
+# Build generated code
+$(BUILD_DIR)/libcodegen.a: $(CODEGEN_SRCS)
+	$(QUIET)cd $(abspath $(BUILD_DIR)/codegen/host/src) && $(CC) -c $(PKG_CFLAGS) $(CODEGEN_SRCS)
+	$(QUIET)$(AR) -cr $(abspath $(BUILD_DIR)/libcodegen.a) $(CODEGEN_OBJS)
+	$(QUIET)$(RANLIB) $(abspath $(BUILD_DIR)/libcodegen.a)
+
+# Build CMSIS startup code
+${BUILD_DIR}/libcmsis_startup.a: $(CMSIS_STARTUP_SRCS)
+	$(QUIET)mkdir -p $(abspath $(BUILD_DIR)/libcmsis_startup)
+	$(QUIET)cd $(abspath $(BUILD_DIR)/libcmsis_startup) && $(CC) -c $(PKG_CFLAGS) -D${ARM_CPU} $^
+	$(QUIET)$(AR) -cr $(abspath $(BUILD_DIR)/libcmsis_startup.a) $(abspath $(BUILD_DIR))/libcmsis_startup/*.o
+	$(QUIET)$(RANLIB) $(abspath $(BUILD_DIR)/libcmsis_startup.a)
+
+CMSIS_SHA_FILE=${CMSIS_PATH}/977abe9849781a2e788b02282986480ff4e25ea6.sha
+ifneq ("$(wildcard $(CMSIS_SHA_FILE))","")
+${BUILD_DIR}/cmsis_nn/Source/libcmsis-nn.a:
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)cd $(CMSIS_PATH)/CMSIS/NN && $(CMAKE) -B $(abspath $(BUILD_DIR)/cmsis_nn) $(CMSIS_NN_CMAKE_FLAGS)
+	$(QUIET)cd $(abspath $(BUILD_DIR)/cmsis_nn) && $(MAKE) all
+else
+# Build CMSIS-NN
+${BUILD_DIR}/cmsis_nn/Source/SoftmaxFunctions/libCMSISNNSoftmax.a:
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)cd $(CMSIS_PATH)/CMSIS/NN && $(CMAKE) -B $(abspath $(BUILD_DIR)/cmsis_nn) $(CMSIS_NN_CMAKE_FLAGS)
+	$(QUIET)cd $(abspath $(BUILD_DIR)/cmsis_nn) && $(MAKE) all
+endif
+
+# Build demo application
+ifneq ("$(wildcard $(CMSIS_SHA_FILE))","")
+$(BUILD_DIR)/demo: $(DEMO_MAIN) $(UART_SRCS) $(BUILD_DIR)/stack_allocator.o $(BUILD_DIR)/crt_backend_api.o \
+	${BUILD_DIR}/libcodegen.a ${BUILD_DIR}/libcmsis_startup.a ${BUILD_DIR}/cmsis_nn/Source/libcmsis-nn.a
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)$(CC) $(PKG_CFLAGS) $(FREERTOS_FLAGS) -o $@ -Wl,--whole-archive $^ -Wl,--no-whole-archive $(PKG_LDFLAGS)
+else
+$(BUILD_DIR)/demo: $(DEMO_MAIN) $(UART_SRCS) $(BUILD_DIR)/stack_allocator.o $(BUILD_DIR)/crt_backend_api.o \
+       ${BUILD_DIR}/libcodegen.a ${BUILD_DIR}/libcmsis_startup.a \
+       ${BUILD_DIR}/cmsis_nn/Source/SoftmaxFunctions/libCMSISNNSoftmax.a \
+       ${BUILD_DIR}/cmsis_nn/Source/FullyConnectedFunctions/libCMSISNNFullyConnected.a \
+       ${BUILD_DIR}/cmsis_nn/Source/SVDFunctions/libCMSISNNSVDF.a \
+       ${BUILD_DIR}/cmsis_nn/Source/ReshapeFunctions/libCMSISNNReshape.a \
+       ${BUILD_DIR}/cmsis_nn/Source/ActivationFunctions/libCMSISNNActivation.a \
+       ${BUILD_DIR}/cmsis_nn/Source/NNSupportFunctions/libCMSISNNSupport.a \
+       ${BUILD_DIR}/cmsis_nn/Source/ConcatenationFunctions/libCMSISNNConcatenation.a \
+       ${BUILD_DIR}/cmsis_nn/Source/BasicMathFunctions/libCMSISNNBasicMaths.a \
+       ${BUILD_DIR}/cmsis_nn/Source/ConvolutionFunctions/libCMSISNNConvolutions.a \
+       ${BUILD_DIR}/cmsis_nn/Source/PoolingFunctions/libCMSISNNPooling.a
+	$(QUIET)mkdir -p $(@D)
+	$(QUIET)$(CC) $(PKG_CFLAGS) $(FREERTOS_FLAGS) -o $@ -Wl,--whole-archive $^ -Wl,--no-whole-archive $(PKG_LDFLAGS)
+endif
+
+clean:
+	$(QUIET)rm -rf $(BUILD_DIR)/codegen
+
+cleanall:
+	$(QUIET)rm -rf $(BUILD_DIR)
+
+.SUFFIXES:
+
+.DEFAULT: demo

deploy/avh/README.md

@@ -0,0 +1,114 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+English | [简体中文](README_ch.md)
+
+Running PaddleOCR text recognition model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware
+======================================================================
+
+This folder contains an example of how to run a PaddleOCR model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware).
+
+
+Running environment and prerequisites
+-------------
+Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script.
+You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance.
+
+Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed.
+
+Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following:
+- Software required to build and run the demo (These can all be installed by running
+  tvm/docker/install/ubuntu_install_ethosu_driver_stack.sh.)
+  - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
+  - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/)
+  - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2)
+  - [Arm(R) Ethos(TM)-U NPU driver stack](https://review.mlplatform.org)
+  - [CMSIS](https://github.com/ARM-software/CMSIS_5)
+- The python libraries listed in the requirements.txt of this directory
+  - These can be installed by running the following from the current directory:
+    ```bash
+    pip install -r ./requirements.txt
+    ```
+
+In case2 and case3:
+
+You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP.
+For example if you've installed these in ```/opt/arm``` , then you would do the following:
+```bash
+export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH
+```
+
+You will also need TVM which can either be:
+  - Installed from TLCPack(see [TLCPack](https://tlcpack.ai/))
+  - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html))
+    - When building from source, the following need to be set in config.cmake:
+      - set(USE_CMSISNN ON)
+      - set(USE_MICRO ON)
+      - set(USE_LLVM ON)
+
+
+Running the demo application
+----------------------------
+Type the following command to run the bare metal text recognition application ([src/demo_bare_metal.c](./src/demo_bare_metal.c)):
+
+```bash
+./run_demo.sh
+```
+
+If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables.
+
+```bash
+./run_demo.sh --enable_FVP 1
+```
+
+If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then
+the locations for these can be specified as arguments to run_demo.sh, for example:
+
+```bash
+./run_demo.sh --cmsis_path /home/tvm-user/cmsis \
+--ethosu_platform_path /home/tvm-user/ethosu/core_platform
+```
+
+With [run_demo.sh](./run_demo.sh) to run the demo application, it will:
+- Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1)
+- Download a PaddleOCR text recognition model
+- Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN
+- Create a C header file inputs.c containing the image data as a C array
+- Create a C header file outputs.c containing a C array where the output of inference will be stored
+- Build the demo application
+- Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software
+- The application will report the text on the image and the corresponding score.
+
+Using your own image
+--------------------
+The create_image.py script takes a single argument on the command line which is the path of the
+image to be converted into an array of bytes for consumption by the model.
+
+The demo can be modified to use an image of your choice by changing the following line in run_demo.sh
+
+```bash
+python3 ./convert_image.py path/to/image
+```
+
+Model description
+-----------------
+The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current 2.7M English recognition model.
+
+PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features:
+  - PP-OCRv3: ultra-lightweight OCR system: detection (3.6M) + direction classifier (1.4M) + recognition (12M) = 17.0M
+  - Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details
+  - Support vertical text recognition, and long text recognition

deploy/avh/README_ch.md

@@ -0,0 +1,94 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+[English](README.md) | 简体中文
+
+通过TVM在 Arm(R) Cortex(R)-M55 CPU 上运行 PaddleOCR文 本能识别模型
+===============================================================
+
+此文件夹包含如何使用 TVM 在 Cortex(R)-M55 CPU 上运行 PaddleOCR 模型的示例。
+
+依赖
+-------------
+本demo运行在TVM提供的docker环境上，在该环境中已经安装好的必须的软件
+
+
+在非docker环境中，需要手动安装如下依赖项:
+
+- 软件可通过[安装脚本](https://github.com/apache/tvm/blob/main/docker/install/ubuntu_install_ethosu_driver_stack.sh)一键安装
+  - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
+  - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/)
+  - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2)
+  - [Arm(R) Ethos(TM)-U NPU driver stack](https://review.mlplatform.org)
+  - [CMSIS](https://github.com/ARM-software/CMSIS_5)
+- python 依赖
+  ```bash
+  pip install -r ./requirements.txt
+  ```
+- TVM
+  - 从源码安装([Install from Source](https://tvm.apache.org/docs/install/from_source.html))
+    从源码安装时，需要设置如下字段
+      - set(USE_CMSISNN ON)
+      - set(USE_MICRO ON)
+      - set(USE_LLVM ON)
+  - 从TLCPack 安装([TLCPack](https://tlcpack.ai/))
+
+安装完成后需要更新环境变量，以软件安装地址为`/opt/arm`为例：
+```bash
+export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH
+```
+
+运行demo
+----------------------------
+使用如下命令可以一键运行demo
+
+```bash
+./run_demo.sh
+```
+
+如果 Ethos(TM)-U 平台或 CMSIS 没有安装在 `/opt/arm/ethosu` 中，可通过参数进行设置，例如：
+
+```bash
+./run_demo.sh --cmsis_path /home/tvm-user/cmsis \
+--ethosu_platform_path /home/tvm-user/ethosu/core_platform
+```
+
+`./run_demo.sh`脚本会执行如下步骤:
+- 下载 PaddleOCR 文字识别模型
+- 使用tvm将PaddleOCR 文字识别模型编译为 Cortex(R)-M55 CPU 和 CMSIS-NN 后端的可执行文件
+- 创建一个包含输入图像数据的头文件`inputs.c`
+- 创建一个包含输出tensor大小的头文件`outputs.c`
+- 编译可执行程序
+- 运行程序
+- 输出图片上的文字和置信度
+
+使用自己的图片
+--------------------
+替换 `run_demo.sh ` 中140行处的图片地址即可
+
+使用自己的模型
+--------------------
+替换 `run_demo.sh ` 中130行处的模型地址即可
+
+模型描述
+-----------------
+
+在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3原始文本识别模型的基础上进行适配，最终模型大小为2.7M。
+
+PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本，该系列模型具有以下特点：
+   - 超轻量级OCR系统：检测（3.6M）+方向分类器（1.4M）+识别（12M）=17.0M。
+   - 支持80多种多语言识别模型，包括英文、中文、法文、德文、阿拉伯文、韩文、日文等。
+   - 支持竖排文本识别，长文本识别。

deploy/avh/arm-none-eabi-gcc.cmake

@@ -0,0 +1,79 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+if (__TOOLCHAIN_LOADED)
+    return()
+endif()
+set(__TOOLCHAIN_LOADED TRUE)
+
+set(CMAKE_SYSTEM_NAME Generic)
+set(CMAKE_C_COMPILER "arm-none-eabi-gcc")
+set(CMAKE_CXX_COMPILER "arm-none-eabi-g++")
+set(CMAKE_SYSTEM_PROCESSOR "cortex-m55" CACHE STRING "Select Arm(R) Cortex(R)-M architecture. (cortex-m0, cortex-m3, cortex-m33, cortex-m4, cortex-m55, cortex-m7, etc)")
+
+set(CMAKE_TRY_COMPILE_TARGET_TYPE STATIC_LIBRARY)
+
+SET(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
+SET(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
+SET(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
+
+set(CMAKE_C_STANDARD 99)
+set(CMAKE_CXX_STANDARD 14)
+
+# The system processor could for example be set to cortex-m33+nodsp+nofp.
+set(__CPU_COMPILE_TARGET ${CMAKE_SYSTEM_PROCESSOR})
+string(REPLACE "+" ";" __CPU_FEATURES ${__CPU_COMPILE_TARGET})
+list(POP_FRONT __CPU_FEATURES CMAKE_SYSTEM_PROCESSOR)
+
+string(FIND ${__CPU_COMPILE_TARGET} "+" __OFFSET)
+if(__OFFSET GREATER_EQUAL 0)
+    string(SUBSTRING ${__CPU_COMPILE_TARGET} ${__OFFSET} -1 CPU_FEATURES)
+endif()
+
+# Add -mcpu to the compile options to override the -mcpu the CMake toolchain adds
+add_compile_options(-mcpu=${__CPU_COMPILE_TARGET})
+
+# Set floating point unit
+if("${__CPU_COMPILE_TARGET}" MATCHES "\\+fp")
+    set(FLOAT hard)
+elseif("${__CPU_COMPILE_TARGET}" MATCHES "\\+nofp")
+    set(FLOAT soft)
+elseif("${CMAKE_SYSTEM_PROCESSOR}" STREQUAL "cortex-m33" OR
+       "${CMAKE_SYSTEM_PROCESSOR}" STREQUAL "cortex-m55")
+    set(FLOAT hard)
+else()
+    set(FLOAT soft)
+endif()
+
+add_compile_options(-mfloat-abi=${FLOAT})
+add_link_options(-mfloat-abi=${FLOAT})
+
+# Link target
+add_link_options(-mcpu=${__CPU_COMPILE_TARGET})
+add_link_options(-Xlinker -Map=output.map)
+
+#
+# Compile options
+#
+set(cxx_flags "-fno-unwind-tables;-fno-rtti;-fno-exceptions")
+
+add_compile_options("-Wall;-Wextra;-Wsign-compare;-Wunused;-Wswitch-default;\
+-Wdouble-promotion;-Wredundant-decls;-Wshadow;-Wnull-dereference;\
+-Wno-format-extra-args;-Wno-unused-function;-Wno-unused-label;\
+-Wno-missing-field-initializers;-Wno-return-type;-Wno-format;-Wno-int-conversion"
+    "$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
+)

deploy/avh/configure_avh.sh

@@ -0,0 +1,79 @@
+#!/bin/bash
+# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved.
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -e
+set -u
+set -o pipefail
+
+# Show usage
+function show_usage() {
+    cat <<EOF
+Usage: Set up running environment by installing the required prerequisites.
+-h, --help
+    Display this help message.
+EOF
+}
+
+if [ "$#" -eq 1 ] && [ "$1" == "--help" -o "$1" == "-h" ]; then
+    show_usage
+    exit 0
+elif [ "$#" -ge 1 ]; then
+        show_usage
+        exit 1
+fi
+
+echo -e "\e[36mStart setting up running environment\e[0m"
+
+# Install CMSIS
+echo -e "\e[36mStart installing CMSIS\e[0m"
+CMSIS_PATH="/opt/arm/ethosu/cmsis" 
+mkdir -p "${CMSIS_PATH}"
+
+CMSIS_SHA="977abe9849781a2e788b02282986480ff4e25ea6"
+CMSIS_SHASUM="86c88d9341439fbb78664f11f3f25bc9fda3cd7de89359324019a4d87d169939eea85b7fdbfa6ad03aa428c6b515ef2f8cd52299ce1959a5444d4ac305f934cc"
+CMSIS_URL="http://github.com/ARM-software/CMSIS_5/archive/${CMSIS_SHA}.tar.gz"
+DOWNLOAD_PATH="/tmp/${CMSIS_SHA}.tar.gz"
+
+wget ${CMSIS_URL} -O "${DOWNLOAD_PATH}"
+echo "$CMSIS_SHASUM" ${DOWNLOAD_PATH} | sha512sum -c
+tar -xf "${DOWNLOAD_PATH}" -C "${CMSIS_PATH}" --strip-components=1
+touch "${CMSIS_PATH}"/"${CMSIS_SHA}".sha
+echo -e "\e[36mCMSIS Installation SUCCESS\e[0m"
+
+# Install Arm(R) Ethos(TM)-U NPU driver stack
+echo -e "\e[36mStart installing Arm(R) Ethos(TM)-U NPU driver stack\e[0m"
+git clone "https://review.mlplatform.org/ml/ethos-u/ethos-u-core-platform" /opt/arm/ethosu/core_platform 
+cd /opt/arm/ethosu/core_platform 
+git checkout tags/"21.11"
+echo -e "\e[36mArm(R) Ethos(TM)-U Core Platform Installation SUCCESS\e[0m"
+ 
+# Install Arm(R) GNU Toolchain
+echo -e "\e[36mStart installing Arm(R) GNU Toolchain\e[0m"
+mkdir -p /opt/arm/gcc-arm-none-eabi
+export gcc_arm_url='https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2?revision=ca0cbf9c-9de2-491c-ac48-898b5bbc0443&la=en&hash=68760A8AE66026BCF99F05AC017A6A50C6FD832A'
+curl --retry 64 -sSL ${gcc_arm_url} | tar -C /opt/arm/gcc-arm-none-eabi --strip-components=1 -jx 
+export PATH=/opt/arm/gcc-arm-none-eabi/bin:$PATH 
+arm-none-eabi-gcc --version
+arm-none-eabi-g++ --version
+echo -e "\e[36mArm(R) Arm(R) GNU Toolchain Installation SUCCESS\e[0m"
+
+# Install TVM from TLCPack
+echo -e "\e[36mStart installing TVM\e[0m"
+pip install tlcpack-nightly -f https://tlcpack.ai/wheels
+echo -e "\e[36mTVM Installation SUCCESS\e[0m"

deploy/avh/convert_image.py

@@ -0,0 +1,102 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import pathlib
+import re
+import sys
+import cv2
+import math
+from PIL import Image
+import numpy as np
+
+
+def resize_norm_img(img, image_shape, padding=True):
+    imgC, imgH, imgW = image_shape
+    h = img.shape[0]
+    w = img.shape[1]
+    if not padding:
+        resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_w = imgW
+    else:
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+    resized_image = resized_image.astype("float32")
+    if image_shape[0] == 1:
+        resized_image = resized_image / 255
+        resized_image = resized_image[np.newaxis, :]
+    else:
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+    padding_im[:, :, 0:resized_w] = resized_image
+    return padding_im
+
+
+def create_header_file(name, tensor_name, tensor_data, output_path):
+    """
+    This function generates a header file containing the data from the numpy array provided.
+    """
+    file_path = pathlib.Path(f"{output_path}/" + name).resolve()
+    # Create header file with npy_data as a C array
+    raw_path = file_path.with_suffix(".h").resolve()
+    with open(raw_path, "w") as header_file:
+        header_file.write(
+            "\n"
+            + f"const size_t {tensor_name}_len = {tensor_data.size};\n"
+            + f'__attribute__((section(".data.tvm"), aligned(16))) float {tensor_name}[] = '
+        )
+
+        header_file.write("{")
+        for i in np.ndindex(tensor_data.shape):
+            header_file.write(f"{tensor_data[i]}, ")
+        header_file.write("};\n\n")
+
+
+def create_headers(image_name):
+    """
+    This function generates C header files for the input and output arrays required to run inferences
+    """
+    img_path = os.path.join("./", f"{image_name}")
+
+    # Resize image to 32x320
+    img = cv2.imread(img_path)
+    img = resize_norm_img(img, [3, 32, 320])
+    img_data = img.astype("float32")
+
+    # # Add the batch dimension, as we are expecting 4-dimensional input: NCHW.
+    img_data = np.expand_dims(img_data, axis=0)
+
+    # Create input header file
+    create_header_file("inputs", "input", img_data, "./include")
+    # Create output header file
+    output_data = np.zeros([7760], np.float32)
+    create_header_file(
+        "outputs",
+        "output",
+        output_data,
+        "./include",
+    )
+
+
+if __name__ == "__main__":
+    create_headers(sys.argv[1])

deploy/avh/corstone300.ld

@@ -0,0 +1,295 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*------------------ Reference System Memories -------------
+  +===================+============+=======+============+============+
+  | Memory            | Address    | Size  | CPU Access | NPU Access |
+  +===================+============+=======+============+============+
+  | ITCM              | 0x00000000 | 512KB | Yes (RO)   | No         |
+  +-------------------+------------+-------+------------+------------+
+  | DTCM              | 0x20000000 | 512KB | Yes (R/W)  | No         |
+  +-------------------+------------+-------+------------+------------+
+  | SSE-300 SRAM      | 0x21000000 |   2MB | Yes (R/W)  | Yes (R/W)  |
+  +-------------------+------------+-------+------------+------------+
+  | Data SRAM         | 0x01000000 |   2MB | Yes (R/W)  | Yes (R/W)  |
+  +-------------------+------------+-------+------------+------------+
+  | DDR               | 0x60000000 |  32MB | Yes (R/W)  | Yes (R/W)  |
+  +-------------------+------------+-------+------------+------------+ */
+
+/*---------------------- ITCM Configuration ----------------------------------
+  <h> Flash Configuration
+    <o0> Flash Base Address <0x0-0xFFFFFFFF:8>
+    <o1> Flash Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+  -----------------------------------------------------------------------------*/
+__ROM_BASE = 0x00000000;
+__ROM_SIZE = 0x00080000;
+
+/*--------------------- DTCM RAM Configuration ----------------------------
+  <h> RAM Configuration
+    <o0> RAM Base Address    <0x0-0xFFFFFFFF:8>
+    <o1> RAM Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+ -----------------------------------------------------------------------------*/
+__RAM_BASE = 0x20000000;
+__RAM_SIZE = 0x00080000;
+
+/*----------------------- Data SRAM Configuration ------------------------------
+  <h> Data SRAM Configuration
+    <o0> DATA_SRAM Base Address    <0x0-0xFFFFFFFF:8>
+    <o1> DATA_SRAM Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+ -----------------------------------------------------------------------------*/
+__DATA_SRAM_BASE = 0x01000000;
+__DATA_SRAM_SIZE = 0x00200000;
+
+/*--------------------- Embedded SRAM Configuration ----------------------------
+  <h> SRAM Configuration
+    <o0> SRAM Base Address    <0x0-0xFFFFFFFF:8>
+    <o1> SRAM Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+ -----------------------------------------------------------------------------*/
+__SRAM_BASE = 0x21000000;
+__SRAM_SIZE = 0x00200000;
+
+/*--------------------- Stack / Heap Configuration ----------------------------
+  <h> Stack / Heap Configuration
+    <o0> Stack Size (in Bytes) <0x0-0xFFFFFFFF:8>
+    <o1> Heap Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+  -----------------------------------------------------------------------------*/
+__STACK_SIZE = 0x00008000;
+__HEAP_SIZE  = 0x00008000;
+
+/*--------------------- Embedded RAM Configuration ----------------------------
+  <h> DDR Configuration
+    <o0> DDR Base Address    <0x0-0xFFFFFFFF:8>
+    <o1> DDR Size (in Bytes) <0x0-0xFFFFFFFF:8>
+  </h>
+ -----------------------------------------------------------------------------*/
+__DDR_BASE = 0x60000000;
+__DDR_SIZE = 0x02000000;
+
+/*
+ *-------------------- <<< end of configuration section >>> -------------------
+ */
+
+MEMORY
+{
+  ITCM       (rx)  : ORIGIN = __ROM_BASE, LENGTH = __ROM_SIZE
+  DTCM       (rwx) : ORIGIN = __RAM_BASE, LENGTH = __RAM_SIZE
+  DATA_SRAM  (rwx) : ORIGIN = __DATA_SRAM_BASE, LENGTH = __DATA_SRAM_SIZE
+  SRAM       (rwx) : ORIGIN = __SRAM_BASE, LENGTH = __SRAM_SIZE
+  DDR        (rwx) : ORIGIN = __DDR_BASE, LENGTH = __DDR_SIZE
+}
+
+/* Linker script to place sections and symbol values. Should be used together
+ * with other linker script that defines memory regions ITCM and RAM.
+ * It references following symbols, which must be defined in code:
+ *   Reset_Handler : Entry of reset handler
+ *
+ * It defines following symbols, which code can use without definition:
+ *   __exidx_start
+ *   __exidx_end
+ *   __copy_table_start__
+ *   __copy_table_end__
+ *   __zero_table_start__
+ *   __zero_table_end__
+ *   __etext
+ *   __data_start__
+ *   __preinit_array_start
+ *   __preinit_array_end
+ *   __init_array_start
+ *   __init_array_end
+ *   __fini_array_start
+ *   __fini_array_end
+ *   __data_end__
+ *   __bss_start__
+ *   __bss_end__
+ *   __end__
+ *   end
+ *   __HeapLimit
+ *   __StackLimit
+ *   __StackTop
+ *   __stack
+ */
+ENTRY(Reset_Handler)
+
+SECTIONS
+{
+  /* .ddr is placed before .text so that .rodata.tvm is encountered before .rodata* */
+  .ddr :
+  {
+    . = ALIGN (16);
+    *(.rodata.tvm)
+    . = ALIGN (16);
+    *(.data.tvm);
+    . = ALIGN(16);    
+  } > DDR
+
+  .text :
+  {
+    KEEP(*(.vectors))
+    *(.text*)
+
+    KEEP(*(.init))
+    KEEP(*(.fini))
+
+    /* .ctors */
+    *crtbegin.o(.ctors)
+    *crtbegin?.o(.ctors)
+    *(EXCLUDE_FILE(*crtend?.o *crtend.o) .ctors)
+    *(SORT(.ctors.*))
+    *(.ctors)
+
+    /* .dtors */
+    *crtbegin.o(.dtors)
+    *crtbegin?.o(.dtors)
+    *(EXCLUDE_FILE(*crtend?.o *crtend.o) .dtors)
+    *(SORT(.dtors.*))
+    *(.dtors)
+
+    *(.rodata*)
+
+    KEEP(*(.eh_frame*))
+  } > ITCM
+
+  .ARM.extab :
+  {
+    *(.ARM.extab* .gnu.linkonce.armextab.*)
+  } > ITCM
+
+  __exidx_start = .;
+  .ARM.exidx :
+  {
+    *(.ARM.exidx* .gnu.linkonce.armexidx.*)
+  } > ITCM
+  __exidx_end = .;
+
+  .copy.table :
+  {
+    . = ALIGN(4);
+    __copy_table_start__ = .;
+    LONG (__etext)
+    LONG (__data_start__)
+    LONG (__data_end__ - __data_start__)
+    /* Add each additional data section here */
+    __copy_table_end__ = .;
+  } > ITCM
+
+  .zero.table :
+  {
+    . = ALIGN(4);
+    __zero_table_start__ = .;
+    __zero_table_end__ = .;
+  } > ITCM
+
+  /**
+   * Location counter can end up 2byte aligned with narrow Thumb code but
+   * __etext is assumed by startup code to be the LMA of a section in DTCM
+   * which must be 4byte aligned
+   */
+  __etext = ALIGN (4);
+
+  .sram :
+  {
+    . = ALIGN(16);
+  } > SRAM AT > SRAM
+
+  .data : AT (__etext)
+  {
+    __data_start__ = .;
+    *(vtable)
+    *(.data)
+    *(.data.*)
+
+    . = ALIGN(4);
+    /* preinit data */
+    PROVIDE_HIDDEN (__preinit_array_start = .);
+    KEEP(*(.preinit_array))
+    PROVIDE_HIDDEN (__preinit_array_end = .);
+
+    . = ALIGN(4);
+    /* init data */
+    PROVIDE_HIDDEN (__init_array_start = .);
+    KEEP(*(SORT(.init_array.*)))
+    KEEP(*(.init_array))
+    PROVIDE_HIDDEN (__init_array_end = .);
+
+
+    . = ALIGN(4);
+    /* finit data */
+    PROVIDE_HIDDEN (__fini_array_start = .);
+    KEEP(*(SORT(.fini_array.*)))
+    KEEP(*(.fini_array))
+    PROVIDE_HIDDEN (__fini_array_end = .);
+
+    KEEP(*(.jcr*))
+    . = ALIGN(4);
+    /* All data end */
+    __data_end__ = .;
+
+  } > DTCM
+
+  .bss.noinit (NOLOAD):
+  {
+    . = ALIGN(16);
+    *(.bss.noinit.*)
+    . = ALIGN(16);
+  } > SRAM AT > SRAM
+
+  .bss :
+  {
+    . = ALIGN(4);
+    __bss_start__ = .;
+    *(.bss)
+    *(.bss.*)
+    *(COMMON)
+    . = ALIGN(4);
+    __bss_end__ = .;
+  } > DTCM AT > DTCM
+
+  .data_sram :
+  {
+    . = ALIGN(16);
+  } > DATA_SRAM
+
+  .heap (COPY) :
+  {
+    . = ALIGN(8);
+    __end__ = .;
+    PROVIDE(end = .);
+    . = . + __HEAP_SIZE;
+    . = ALIGN(8);
+    __HeapLimit = .;
+  } > DTCM
+
+  .stack (ORIGIN(DTCM) + LENGTH(DTCM) - __STACK_SIZE) (COPY) :
+  {
+    . = ALIGN(8);
+    __StackLimit = .;
+    . = . + __STACK_SIZE;
+    . = ALIGN(8);
+    __StackTop = .;
+  } > DTCM
+  PROVIDE(__stack = __StackTop);
+
+  /* Check if data + stack exceeds DTCM limit */
+  ASSERT(__StackLimit >= __bss_end__, "region DTCM overflowed with stack")
+}

deploy/avh/include/crt_config.h

@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#ifndef TVM_RUNTIME_CRT_CONFIG_H_
+#define TVM_RUNTIME_CRT_CONFIG_H_
+
+/*! Log level of the CRT runtime */
+#define TVM_CRT_LOG_LEVEL TVM_CRT_LOG_LEVEL_DEBUG
+
+#endif // TVM_RUNTIME_CRT_CONFIG_H_

deploy/avh/include/tvm_runtime.h

@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <stdarg.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/crt/stack_allocator.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void __attribute__((noreturn)) TVMPlatformAbort(tvm_crt_error_t error_code) {
+  printf("TVMPlatformAbort: %d\n", error_code);
+  printf("EXITTHESIM\n");
+  exit(-1);
+}
+
+tvm_crt_error_t TVMPlatformMemoryAllocate(size_t num_bytes, DLDevice dev,
+                                          void **out_ptr) {
+  return kTvmErrorFunctionCallNotImplemented;
+}
+
+tvm_crt_error_t TVMPlatformMemoryFree(void *ptr, DLDevice dev) {
+  return kTvmErrorFunctionCallNotImplemented;
+}
+
+void TVMLogf(const char *msg, ...) {
+  va_list args;
+  va_start(args, msg);
+  vfprintf(stdout, msg, args);
+  va_end(args);
+}
+
+TVM_DLL int TVMFuncRegisterGlobal(const char *name, TVMFunctionHandle f,
+                                  int override) {
+  return 0;
+}
+
+#ifdef __cplusplus
+}
+#endif

deploy/avh/requirements.txt

@@ -0,0 +1,4 @@
+paddlepaddle
+numpy
+opencv-python
+typing-extensions

deploy/avh/run_demo.sh

@@ -0,0 +1,184 @@
+#!/bin/bash
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+set -e
+set -u
+set -o pipefail
+
+# Show usage
+function show_usage() {
+    cat <<EOF
+Usage: run_demo.sh
+-h, --help
+	Display this help message.
+--cmsis_path CMSIS_PATH
+	Set path to CMSIS.
+--ethosu_platform_path ETHOSU_PLATFORM_PATH
+	Set path to Arm(R) Ethos(TM)-U core platform.
+--fvp_path FVP_PATH
+	Set path to FVP.
+--cmake_path
+	Set path to cmake.
+--enable_FVP
+	Set 1 to run application on local Fixed Virtual Platforms (FVPs) executables.
+EOF
+}
+
+# Configure environment variables
+FVP_enable=0
+export PATH=/opt/arm/gcc-arm-none-eabi/bin:$PATH
+
+# Install python libraries
+echo -e "\e[36mInstall python libraries\e[0m"
+sudo pip install -r ./requirements.txt
+
+# Parse arguments
+while (( $# )); do
+    case "$1" in
+        -h|--help)
+            show_usage
+            exit 0
+            ;;
+
+        --cmsis_path)
+            if [ $# -gt 1 ]
+            then
+                export CMSIS_PATH="$2"
+                shift 2
+            else
+                echo 'ERROR: --cmsis_path requires a non-empty argument' >&2
+                show_usage >&2
+                exit 1
+            fi
+            ;;
+
+        --ethosu_platform_path)
+            if [ $# -gt 1 ]
+            then
+                export ETHOSU_PLATFORM_PATH="$2"
+                shift 2
+            else
+                echo 'ERROR: --ethosu_platform_path requires a non-empty argument' >&2
+                show_usage >&2
+                exit 1
+            fi
+            ;;
+
+        --fvp_path)
+            if [ $# -gt 1 ]
+            then
+                export PATH="$2/models/Linux64_GCC-6.4:$PATH"
+                shift 2
+            else
+                echo 'ERROR: --fvp_path requires a non-empty argument' >&2
+                show_usage >&2
+                exit 1
+            fi
+            ;;
+
+        --cmake_path)
+            if [ $# -gt 1 ]
+            then
+                export CMAKE="$2"
+                shift 2
+            else
+                echo 'ERROR: --cmake_path requires a non-empty argument' >&2
+                show_usage >&2
+                exit 1
+            fi
+            ;;
+            
+        --enable_FVP)
+            if [ $# -gt 1 ] && [ "$2" == "1" -o "$2" == "0" ];
+            then
+                FVP_enable="$2"
+                shift 2
+            else
+                echo 'ERROR: --enable_FVP requires a right argument 1 or 0' >&2
+                show_usage >&2
+                exit 1
+            fi
+            ;;
+
+        -*|--*)
+            echo "Error: Unknown flag: $1" >&2
+            show_usage >&2
+            exit 1
+            ;;
+    esac
+done
+
+# Choose running environment: cloud(default) or local environment
+Platform="VHT_Corstone_SSE-300_Ethos-U55"
+if [ $FVP_enable == "1" ]; then
+	Platform="FVP_Corstone_SSE-300_Ethos-U55"
+	echo -e "\e[36mRun application on local Fixed Virtual Platforms (FVPs)\e[0m"
+else
+	if [ ! -d "/opt/arm/" ]; then
+		sudo ./configure_avh.sh
+	fi
+fi
+
+# Directories
+script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+
+# Make build directory
+make cleanall
+mkdir -p build
+cd build
+
+# Get PaddlePaddle inference model
+echo -e "\e[36mDownload PaddlePaddle inference model\e[0m"
+wget https://paddleocr.bj.bcebos.com/tvm/ocr_en.tar
+tar -xf ocr_en.tar
+
+# Compile model for Arm(R) Cortex(R)-M55 CPU and CMSIS-NN
+# An alternative to using "python3 -m tvm.driver.tvmc" is to call
+# "tvmc" directly once TVM has been pip installed.
+python3 -m tvm.driver.tvmc compile --target=cmsis-nn,c \
+    --target-cmsis-nn-mcpu=cortex-m55 \
+    --target-c-mcpu=cortex-m55 \
+    --runtime=crt \
+    --executor=aot \
+    --executor-aot-interface-api=c \
+    --executor-aot-unpacked-api=1 \
+    --pass-config tir.usmp.enable=1 \
+    --pass-config tir.usmp.algorithm=hill_climb \
+    --pass-config tir.disable_storage_rewrite=1 \
+    --pass-config tir.disable_vectorize=1 ocr_en/inference.pdmodel \
+    --output-format=mlf \
+    --model-format=paddle \
+    --module-name=rec \
+    --input-shapes x:[1,3,32,320] \
+    --output=rec.tar
+tar -xf rec.tar
+
+# Create C header files
+cd ..
+python3 ./convert_image.py imgs_words_en/word_116.png
+
+# Build demo executable
+cd ${script_dir}
+echo ${script_dir}
+make
+
+# Run demo executable on the AVH
+$Platform -C cpu0.CFGDTCMSZ=15 \
+-C cpu0.CFGITCMSZ=15 -C mps3_board.uart0.out_file=\"-\" -C mps3_board.uart0.shutdown_tag=\"EXITTHESIM\" \
+-C mps3_board.visualisation.disable-visualisation=1 -C mps3_board.telnetterminal0.start_telnet=0 \
+-C mps3_board.telnetterminal1.start_telnet=0 -C mps3_board.telnetterminal2.start_telnet=0 -C mps3_board.telnetterminal5.start_telnet=0 \
+./build/demo --stat

deploy/avh/src/demo_bare_metal.c

@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <stdio.h>
+#include <tvm_runtime.h>
+#include <tvmgen_rec.h>
+
+#include "uart.h"
+
+// Header files generated by convert_image.py
+#include "inputs.h"
+#include "outputs.h"
+
+int main(int argc, char **argv) {
+  char dict[] = {"#0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`"
+                 "abcdefghijklmnopqrstuvwxyz{|}~!\"#$%&'()*+,-./  "};
+  int char_dict_nums = 97;
+  uart_init();
+  printf("Starting ocr rec inference\n");
+  struct tvmgen_rec_outputs rec_outputs = {
+      .output = output,
+  };
+  struct tvmgen_rec_inputs rec_inputs = {
+      .x = input,
+  };
+
+  tvmgen_rec_run(&rec_inputs, &rec_outputs);
+
+  // post process
+  int char_nums = output_len / char_dict_nums;
+
+  int last_index = 0;
+  float score = 0.f;
+  int count = 0;
+
+  printf("text: ");
+  for (int i = 0; i < char_nums; i++) {
+    int argmax_idx = 0;
+    float max_value = 0.0f;
+    for (int j = 0; j < char_dict_nums; j++) {
+      if (output[i * char_dict_nums + j] > max_value) {
+        max_value = output[i * char_dict_nums + j];
+        argmax_idx = j;
+      }
+    }
+    if (argmax_idx > 0 && (!(i > 0 && argmax_idx == last_index))) {
+      score += max_value;
+      count += 1;
+      // printf("%d，%f，%c\n", argmax_idx, max_value, dict[argmax_idx]);
+      printf("%c", dict[argmax_idx]);
+    }
+    last_index = argmax_idx;
+  }
+  score /= count;
+  printf(", score: %f\n", score);
+
+  // The FVP will shut down when it receives "EXITTHESIM" on the UART
+  printf("EXITTHESIM\n");
+  while (1 == 1)
+    ;
+  return 0;
+}

deploy/cpp_infer/CMakeLists.txt

@@ -0,0 +1,237 @@
+project(ppocr CXX C)
+cmake_minimum_required(VERSION 3.14)
+
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       ON)
+option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    OFF)
+option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
+option(WITH_TENSORRT "Compile demo with TensorRT."   OFF)
+
+SET(PADDLE_LIB "" CACHE PATH "Location of libraries")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
+SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
+
+set(DEMO_NAME "ppocr")
+
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+if (WITH_MKL)
+    ADD_DEFINITIONS(-DUSE_MKL)
+endif()
+
+if(NOT DEFINED PADDLE_LIB)
+  message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
+endif()
+
+if(NOT DEFINED OPENCV_DIR)
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
+endif()
+
+
+if (WIN32)
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/lib")
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/lib")
+endif ()
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    if(WITH_MKL)
+        set(FLAG_OPENMP "/openmp")
+    endif()
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}")
+    if (WITH_STATIC_LIB)
+        safe_set_static_flag()
+        add_definitions(-DSTATIC_LIB)
+	add_definitions(-DYAML_CPP_STATIC_DEFINE)
+    endif()
+    message("cmake c debug flags " ${CMAKE_C_FLAGS_DEBUG})
+    message("cmake c release flags " ${CMAKE_C_FLAGS_RELEASE})
+    message("cmake cxx debug flags " ${CMAKE_CXX_FLAGS_DEBUG})
+    message("cmake cxx release flags " ${CMAKE_CXX_FLAGS_RELEASE})
+else()
+    if(WITH_MKL)
+        set(FLAG_OPENMP "-fopenmp")
+    endif()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O3 ${FLAG_OPENMP} -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+    message("cmake cxx flags" ${CMAKE_CXX_FLAGS})
+endif()
+
+if (WITH_GPU)
+    if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
+include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
+include_directories("${PADDLE_LIB}/third_party/install/glog/include")
+include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
+include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+include_directories("${PADDLE_LIB}/third_party/install/onnxruntime/include")
+include_directories("${PADDLE_LIB}/third_party/install/paddle2onnx/include")
+include_directories("${PADDLE_LIB}/third_party/install/yaml-cpp/include")
+include_directories("${PADDLE_LIB}/third_party/boost")
+include_directories("${PADDLE_LIB}/third_party/eigen3")
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+
+if (NOT WIN32)
+  if (WITH_TENSORRT AND WITH_GPU)
+     include_directories("${TENSORRT_DIR}/include")
+     link_directories("${TENSORRT_DIR}/lib")
+  endif()
+endif(NOT WIN32)
+
+link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+
+link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
+link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_LIB}/third_party/install/onnxruntime/lib")
+link_directories("${PADDLE_LIB}/third_party/install/paddle2onnx/lib")
+link_directories("${PADDLE_LIB}/third_party/install/yaml-cpp/lib")
+link_directories("${PADDLE_LIB}/paddle/lib")
+
+
+if(WITH_MKL)
+  include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.lib)
+  else ()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    execute_process(COMMAND cp -r ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
+  endif ()
+  set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/onednn")
+  if(EXISTS ${MKLDNN_PATH})
+    include_directories("${MKLDNN_PATH}/include")
+    if (WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else ()
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libdnnl.so.3)
+    endif ()
+  endif()
+else()
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+  else ()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif ()
+endif()
+
+# Note: libpaddle_inference_api.so/a must put before libpaddle_inference.so/a
+if(WITH_STATIC_LIB)
+  if(WIN32)
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
+  else()
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+else()
+  if(WIN32)
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
+  endif()
+endif(WITH_STATIC_LIB)
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags protobuf z xxhash
+        )
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+else()
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags_static libprotobuf xxhash)
+    set(DEPS ${DEPS} libcmt shlwapi)
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+endif(NOT WIN32)
+
+if (EXISTS "${PADDLE_LIB}/third_party/install/yaml-cpp/lib")
+  set(DEPS ${DEPS} yaml-cpp)
+endif()
+
+if(WITH_GPU)
+  if(NOT WIN32)
+    if (WITH_TENSORRT)
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+
+include(FetchContent)
+include(external-cmake/auto-log.cmake)
+include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
+
+AUX_SOURCE_DIRECTORY(./src SRCS)
+add_executable(${DEMO_NAME} ${SRCS})
+target_link_libraries(${DEMO_NAME} ${DEPS})
+
+if (WIN32 AND WITH_MKL)
+    add_custom_command(TARGET ${DEMO_NAME} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/onednn/lib/mkldnn.dll ./mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/onednn/lib/mkldnn.dll ./release/mkldnn.dll
+    )
+endif()

deploy/cpp_infer/docs/windows_vs2019_build.md

@@ -0,0 +1,141 @@
+[English](windows_vs2022_build.md) | 简体中文
+
+- [Visual Studio 2019 Community CMake 编译指南](#visual-studio-2019-community-cmake-编译指南)
+  - [1. 环境准备](#1-环境准备)
+    - [1.1 安装必须环境](#11-安装必须环境)
+    - [1.2 下载 PaddlePaddle C++ 预测库和 Opencv](#12-下载-paddlepaddle-c-预测库和-opencv)
+      - [1.2.1 下载 PaddlePaddle C++ 预测库](#121-下载-paddlepaddle-c-预测库)
+      - [1.2.2 安装配置OpenCV](#122-安装配置opencv)
+      - [1.2.3 下载PaddleOCR代码](#123-下载paddleocr代码)
+  - [2. 开始运行](#2-开始运行)
+    - [Step1: 构建Visual Studio项目](#step1-构建visual-studio项目)
+    - [Step2: 执行cmake配置](#step2-执行cmake配置)
+    - [Step3: 生成Visual Studio 项目](#step3-生成visual-studio-项目)
+    - [Step4: 预测](#step4-预测)
+  - [FAQ](#faq)
+
+# Visual Studio 2019 Community CMake 编译指南
+
+PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测试。微软从`Visual Studio 2017`开始即支持直接管理`CMake`跨平台编译项目，但是直到`2019`才提供了稳定和完全的支持，所以如果你想使用CMake管理项目编译构建，我们推荐你使用`Visual Studio 2019`环境下构建。
+
+**下面所有示例以工作目录为 `D:\projects\cpp`演示**。
+
+## 1. 环境准备
+### 1.1 安装必须环境
+
+* Visual Studio 2019
+* CUDA 10.2，cudnn 7+ （仅在使用GPU版本的预测库时需要）
+* CMake 3.22+
+
+请确保系统已经安装好上述基本软件，我们使用的是`VS2019`的社区版。
+
+### 1.2 下载 PaddlePaddle C++ 预测库和 Opencv
+
+#### 1.2.1 下载 PaddlePaddle C++ 预测库
+
+PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载:  [C++预测库下载列表](https://www.paddlepaddle.org.cn/inference/master/guides/install/download_lib.html#windows)
+
+解压后`D:\projects\paddle_inference`目录包含内容为：
+```
+paddle_inference
+├── paddle # paddle核心库和头文件
+|
+├── third_party # 第三方依赖库和头文件
+|
+└── version.txt # 版本和编译信息
+```
+
+#### 1.2.2 安装配置OpenCV
+
+1. 在OpenCV官网下载适用于Windows平台的Opencv， [下载地址](https://github.com/opencv/opencv/releases)
+2. 运行下载的可执行文件，将OpenCV解压至指定目录，如`D:\projects\cpp\opencv`
+
+#### 1.2.3 下载PaddleOCR代码
+```bash
+git clone -b dygraph https://github.com/PaddlePaddle/PaddleOCR
+```
+
+## 2. 开始运行
+
+### Step1: 构建Visual Studio项目
+cmake安装完后后系统里会有一个cmake-gui程序，打开cmake-gui，在第一个输入框处填写源代码路径，第二个输入框处填写编译输出路径
+
+![step1](imgs/cmake_step1.png)
+
+### Step2: 执行cmake配置
+点击界面下方的`Configure`按钮，第一次点击会弹出提示框进行Visual Studio配置，如下图，选择你的Visual Studio版本即可，目标平台选择x64。然后点击`finish`按钮即开始自动执行配置。
+
+![step2](imgs/cmake_step2.png)
+
+第一次执行会报错，这是正常现象，接下来进行Opencv和预测库的配置
+
+* cpu版本，仅需考虑OPENCV_DIR、OpenCV_DIR、PADDLE_LIB三个参数
+
+  - OPENCV_DIR：填写opencv lib文件夹所在位置
+  - OpenCV_DIR：同填写opencv lib文件夹所在位置
+  - PADDLE_LIB：paddle_inference文件夹所在位置
+
+* GPU版本，在cpu版本的基础上，还需填写以下变量
+CUDA_LIB、CUDNN_LIB、TENSORRT_DIR、WITH_GPU、WITH_TENSORRT
+
+- CUDA_LIB: CUDA地址，如 `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64`
+- CUDNN_LIB: 和CUDA_LIB一致
+- TENSORRT_DIR：TRT下载后解压缩的位置，如 `D:\TensorRT-8.0.1.6`
+- WITH_GPU: 打钩
+- WITH_TENSORRT：打勾
+
+配置好的截图如下
+
+![step3](imgs/cmake_step3.png)
+
+配置完成后，再次点击`Configure`按钮。
+
+**注意：**
+  1. 如果使用的是`openblas`版本，请把`WITH_MKL`勾去掉
+  2. 遇到报错 `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.`， 将 `deploy/cpp_infer/external-cmake/auto-log.cmake` 中的github地址改为 https://gitee.com/Double_V/AutoLog 地址即可。
+
+### Step3: 生成Visual Studio 项目
+
+点击`Generate`按钮即可生成Visual Studio 项目的sln文件。
+![step4](imgs/cmake_step4.png)
+
+点击`Open Project`按钮即可在Visual Studio 中打开项目。打开后截图如下
+
+![step5](imgs/vs_step1.png)
+
+在开始生成解决方案之前，执行下面步骤：
+1. 将`Debug`改为`Release`
+2. 下载[dirent.h](https://paddleocr.bj.bcebos.com/deploy/cpp_infer/cpp_files/dirent.h)，并拷贝到 Visual Studio 的 include 文件夹下，如`C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\VS\include`。
+
+点击`生成->生成解决方案`，即可在`build/Release/`文件夹下看见`ppocr.exe`文件。
+
+运行之前，将下面文件拷贝到`build/Release/`文件夹下
+1. `paddle_inference/paddle/lib/paddle_inference.dll`
+2. `paddle_inference/third_party/install/onnxruntime/lib/onnxruntime.dll`
+3. `paddle_inference/third_party/install/paddle2onnx/lib/paddle2onnx.dll`
+4. `opencv/build/x64/vc15/bin/opencv_world455.dll`
+5. 如果使用openblas版本的预测库还需要拷贝 `paddle_inference/third_party/install/openblas/lib/openblas.dll`
+
+### Step4: 预测
+
+上述`Visual Studio 2019`编译产出的可执行文件在`build/Release/`目录下，打开`cmd`，并切换到`D:\projects\cpp\PaddleOCR\deploy\cpp_infer\`：
+
+```
+cd /d D:\projects\cpp\PaddleOCR\deploy\cpp_infer
+```
+可执行文件`ppocr.exe`即为样例的预测程序，其主要使用方法如下，更多使用方法可以参考[说明文档](../readme_ch.md)`运行demo`部分。
+
+```shell
+# 切换终端编码为utf8
+CHCP 65001
+# 执行预测
+.\build\Release\ppocr.exe system --det_model_dir=D:\projects\cpp\ch_PP-OCRv2_det_slim_quant_infer --rec_model_dir=D:\projects\cpp\ch_PP-OCRv2_rec_slim_quant_infer --image_dir=D:\projects\cpp\PaddleOCR\doc\imgs\11.jpg
+```
+
+识别结果如下
+![result](imgs/result.jpg)
+
+
+## FAQ
+
+* 运行时，弹窗报错提示`应用程序无法正常启动(0xc0000142)`，并且`cmd`窗口内提示`You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.`，把tensort目录下的lib里面的所有dll文件复制到release目录下，再次运行即可。

deploy/cpp_infer/docs/windows_vs2022_build.md

@@ -0,0 +1,143 @@
+English | [简体中文](windows_vs2019_build.md)
+
+# Visual Studio 2022 Community CMake Compilation Guide
+
+PaddleOCR has been tested on Windows using `Visual Studio 2022 Community`. Microsoft started supporting direct `CMake` project management from `Visual Studio 2017`, but it wasn't fully stable and reliable until `2019`. If you want to use CMake for project management and compilation, we recommend using `Visual Studio 2022`.
+
+**All examples below assume the working directory is **`D:\projects\cpp`**.**
+
+## 1. Environment Preparation
+
+### 1.1 Install Required Dependencies
+
+- Visual Studio 2019 or newer
+- CUDA 10.2, cuDNN 7+ (only required for the GPU version of the prediction library). Additionally, the NVIDIA Computing Toolkit must be installed, and the NVIDIA cuDNN library must be downloaded.
+- CMake 3.22+
+
+Ensure that the above dependencies are installed before proceeding. In this tutorial the Community Edition of `VS2022` was used.
+
+### 1.2 Download PaddlePaddle C++ Prediction Library and OpenCV
+
+#### 1.2.1 Download PaddlePaddle C++ Prediction Library
+
+PaddlePaddle C++ prediction libraries offer different precompiled versions for various `CPU` and `CUDA` configurations. Download the appropriate version from: [C++ Prediction Library Download List](https://www.paddlepaddle.org.cn/inference/master/guides/install/download_lib.html#windows)
+
+After extraction, the `D:\projects\paddle_inference` directory should contain:
+
+```
+paddle_inference
+├── paddle # Core Paddle library and header files
+|
+├── third_party # Third-party dependencies and headers
+|
+└── version.txt # Version and compilation information
+```
+
+#### 1.2.2 Install and Configure OpenCV
+
+1. Download OpenCV for Windows from the [official release page](https://github.com/opencv/opencv/releases).
+2. Run the downloaded executable and extract OpenCV to a specified directory, e.g., `D:\projects\cpp\opencv`.
+
+#### 1.2.3 Download PaddleOCR Code
+
+```bash
+git clone https://github.com/PaddlePaddle/Paddle.git
+git checkout develop
+```
+
+## 2. Running the Project
+
+### Step 1: Create a Visual Studio Project
+
+Once CMake is installed, open the `cmake-gui` application. Specify the source code directory in the first input box and the build output directory in the second input box.
+
+![step1](imgs/cmake_step1.png)
+
+### Step 2: Run CMake Configuration
+
+Click the `Configure` button at the bottom of the interface. The first time you run it, a prompt will appear asking for the Visual Studio configuration. Select your `Visual Studio` version and set the target platform to `x64`. Click `Finish` to start the configuration process.
+
+![step2](imgs/cmake_step2.png)
+
+The first run will result in errors, which is expected. You now need to configure OpenCV and the prediction library.
+
+- **For CPU version**, configure the following variables:
+
+  - `OPENCV_DIR`: Path to the OpenCV `lib` folder
+  - `OpenCV_DIR`: Same as `OPENCV_DIR`
+  - `PADDLE_LIB`: Path to the `paddle_inference` folder
+
+- **For GPU version**, configure additional variables:
+
+  - `CUDA_LIB`: CUDA path, e.g., `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64`
+  - `CUDNN_LIB`: Path to extracted CuDNN library, e.g., `D:\CuDNN-8.9.7.29`
+  - `TENSORRT_DIR`: Path to extracted TensorRT, e.g., `D:\TensorRT-8.0.1.6`
+  - `WITH_GPU`: Check this option
+  - `WITH_TENSORRT`: Check this option
+
+Example configuration:
+
+![step3](imgs/cmake_step3.png)
+
+Once configured, click `Configure` again.
+
+**Note:**
+
+1. If using `openblas`, uncheck `WITH_MKL`.
+2. If you encounter the error `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed`, update `deploy/cpp_infer/external-cmake/auto-log.cmake` to use `https://gitee.com/Double_V/AutoLog`.
+
+### Step 3: Generate Visual Studio Project
+
+Click `Generate` to create the `.sln` file for the Visual Studio project.
+![step4](imgs/cmake_step4.png)
+
+Click `Open Project` to launch the project in Visual Studio. The interface should look like this:
+![step5](imgs/vs_step1.png)
+
+Before building the solution, perform the following steps:
+
+1. Change `Debug` to `Release` mode.
+2. Download [dirent.h](https://paddleocr.bj.bcebos.com/deploy/cpp_infer/cpp_files/dirent.h) and copy it to the Visual Studio include directory, e.g., `C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\include`.
+
+Click `Build -> Build Solution`. Once completed, the `ppocr.exe` file should appear in the `build/Release/` folder.
+
+Before running, copy the following files to `build/Release/`:
+
+1. `paddle_inference/paddle/lib/paddle_inference.dll`
+2. `paddle_inference/paddle/lib/common.dll`
+3. `paddle_inference/third_party/install/mklml/lib/mklml.dll`
+4. `paddle_inference/third_party/install/mklml/lib/libiomp5md.dll`
+5. `paddle_inference/third_party/install/onednn/lib/mkldnn.dll`
+6. `opencv/build/x64/vc15/bin/opencv_world455.dll`
+7. If using the `openblas` version, also copy `paddle_inference/third_party/install/openblas/lib/openblas.dll`.
+
+### Step 4: Run the Prediction
+
+The compiled executable is located in the `build/Release/` directory. Open `cmd` and navigate to `D:\projects\cpp\PaddleOCR\deploy\cpp_infer\`:
+
+```
+cd /d D:\projects\cpp\PaddleOCR\deploy\cpp_infer
+```
+
+Run the prediction using `ppocr.exe`. For more usage details, refer to the [documentation](../readme_ch.md).
+
+```shell
+# Switch terminal encoding to UTF-8
+CHCP 65001
+
+# If using PowerShell, run this command before execution to fix character encoding issues:
+$OutputEncoding = [console]::InputEncoding = [console]::OutputEncoding = New-Object System.Text.UTF8Encoding
+
+# Execute prediction
+.\build\Release\ppocr.exe system --det_model_dir=D:\projects\cpp\ch_PP-OCRv2_det_slim_quant_infer --rec_model_dir=D:\projects\cpp\ch_PP-OCRv2_rec_slim_quant_infer --image_dir=D:\projects\cpp\PaddleOCR\doc\imgs\11.jpg
+```
+
+<br>
+
+## Sample result:
+![result](imgs/result.jpg)
+
+## FAQ
+
+- **Issue:** Application fails to start with error `(0xc0000142)` and `cmd` output shows `You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.`
+- **Solution:** Copy all `.dll` files from the `TensorRT` directory's `lib` folder into the `release` directory and try running it again.