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+[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

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+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.