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docs/datasets/datasets.en.md

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+---
+comments: true
+---
+
+This is a collection of commonly used Chinese datasets, which is being updated continuously. You are welcome to contribute to this list～
+
+In addition to opensource data, users can also use synthesis tools to synthesize data themselves. Current available synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator), etc.
+
+#### 1. ICDAR2019-LSVT
+
+- **Data sources**：<https://ai.baidu.com/broad/introduction?dataset=lsvt>
+- **Introduction**： A total of 45w Chinese street view images, including 5w (2w test + 3w training) fully labeled data (text coordinates + text content), 40w weakly labeled data (text content only), as shown in the following figure:
+    ![](./images/LSVT_1.jpg)
+
+    (a) Fully labeled data
+
+    ![](./images/LSVT_2.jpg)
+
+    (b) Weakly labeled data
+- **Download link**：<https://ai.baidu.com/broad/download?dataset=lsvt>
+
+#### 2. ICDAR2017-RCTW-17
+
+- **Data sources**：<https://rctw.vlrlab.net/>
+- **Introduction**：It contains 12000 + images, most of them are collected in the wild through mobile camera. Some are screenshots. These images show a variety of scenes, including street views, posters, menus, indoor scenes and screenshots of mobile applications.
+    ![](./images/rctw.jpg)
+- **Download link**：<https://rctw.vlrlab.net/dataset/>
+
+#### 3. Chinese Street View Text Recognition
+
+- **Data sources**：<https://aistudio.baidu.com/aistudio/competition/detail/8>
+- **Introduction**：A total of 290000 pictures are included, of which 210000 are used as training sets (with labels) and 80000 are used as test sets (without labels). The dataset is collected from the Chinese street view, and is formed by by cutting out the text line area (such as shop signs, landmarks, etc.) in the street view picture. All the images are preprocessed: by using affine transform, the text area is proportionally mapped to a picture with a height of 48 pixels, as shown in the figure:
+
+    ![](./images/ch_street_rec_1.png)
+
+    (a) Label: 魅派集成吊顶
+
+    ![](./images/ch_street_rec_2.png)
+    (b) Label: 母婴用品连锁
+- **Download link**
+<https://aistudio.baidu.com/aistudio/datasetdetail/8429>
+
+#### 4. Chinese Document Text Recognition
+
+- **Data sources**：<https://github.com/YCG09/chinese_ocr>
+- **Introduction**：
+    - A total of 3.64 million pictures are divided into training set and validation set according to 99:1.
+    - Using Chinese corpus (news + classical Chinese), the data is randomly generated through changes in font, size, grayscale, blur, perspective, stretching, etc.
+    - 5990 characters including Chinese characters, English letters, numbers and punctuation（Characters set: <https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt> ）
+    - Each sample is fixed with 10 characters, and the characters are randomly intercepted from the sentences in the corpus
+    - Image resolution is 280x32
+
+        ![](./images/ch_doc1.jpg)
+
+        ![](./images/ch_doc3.jpg)
+- **Download link**：<https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw> (Password: lu7m)
+
+#### 5、ICDAR2019-ArT
+
+- **Data source**：<https://ai.baidu.com/broad/introduction?dataset=art>
+- **Introduction**：It includes 10166 images, 5603 in training sets and 4563 in test sets. It is composed of three parts: total text, scut-ctw1500 and Baidu curved scene text, including text with various shapes such as horizontal, multi-directional and curved.
+    ![](./images/ArT.jpg)
+- **Download link**：<https://ai.baidu.com/broad/download?dataset=art>
+
+#### 6. Electronic seal dataset
+
+- **Data source**: <https://aistudio.baidu.com/aistudio/datasetdetail/154271/0>
+- **Data introduction**: Contains 10,000 images in total, 8,000 images in the training set, and 2,000 images in the test set. The dataset is synthesized by a program and does not involve privacy security. It is mainly used for the training and detection of seal curved text. Contributed by developer [jingsongliujing](https://github.com/jingsongliujing)
+- **Download address**: <https://aistudio.baidu.com/aistudio/datasetdetail/154271/0>
+
+## References
+
+**ICDAR 2019-LSVT Challenge**
+
+```bibtex
+@article{sun2019icdar,
+  title={ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling--RRC-LSVT},
+  author={Sun, Yipeng and Ni, Zihan and Chng, Chee-Kheng and Liu, Yuliang and Luo, Canjie and Ng, Chun Chet and Han, Junyu and Ding, Errui and Liu, Jingtuo and Karatzas, Dimosthenis and others},
+  journal={arXiv preprint arXiv:1909.07741},
+  year={2019}
+}
+```
+
+**ICDAR 2019-ArT Challenge**
+
+```bibtex
+@article{chng2019icdar2019,
+  title={ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)},
+  author={Chng, Chee-Kheng and Liu, Yuliang and Sun, Yipeng and Ng, Chun Chet and Luo, Canjie and Ni, Zihan and Fang, ChuanMing and Zhang, Shuaitao and Han, Junyu and Ding, Errui and others},
+  journal={arXiv preprint arXiv:1909.07145},
+  year={2019}
+}
+```

docs/datasets/datasets.md

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+---
+comments: true
+---
+
+这里整理了常用中文数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+除了开源数据，用户还可使用合成工具自行合成，可参考[数据合成工具](../data_anno_synth/data_synthesis.md)；
+
+如果需要标注自己的数据，可参考[数据标注工具](../data_anno_synth/data_annotation.md)。
+
+#### 1、ICDAR2019-LSVT
+
+- **数据来源**：<https://ai.baidu.com/broad/introduction?dataset=lsvt>
+- **数据简介**： 共45w中文街景图像，包含5w（2w测试+3w训练）全标注数据（文本坐标+文本内容），40w弱标注数据（仅文本内容），如下图所示：
+    ![](./images/LSVT_1.jpg)
+    (a) 全标注数据
+    ![](./images/LSVT_2.jpg)
+    (b) 弱标注数据
+- **下载地址**：<https://ai.baidu.com/broad/download?dataset=lsvt>
+- **说明**：其中，test数据集的label目前没有开源，如要评估结果，可以去官网提交：<https://rrc.cvc.uab.es/?ch=16>
+
+#### 2、ICDAR2017-RCTW-17
+
+- **数据来源**：<https://rctw.vlrlab.net/>
+- **数据简介**：共包含12,000+图像，大部分图片是通过手机摄像头在野外采集的。有些是截图。这些图片展示了各种各样的场景，包括街景、海报、菜单、室内场景和手机应用程序的截图。
+    ![](./images/rctw.jpg)
+- **下载地址**：<https://rctw.vlrlab.net/dataset/>
+
+#### 3、中文街景文字识别
+
+- **数据来源**：<https://aistudio.baidu.com/aistudio/competition/detail/8>
+- **数据简介**：ICDAR2019-LSVT行识别任务，共包括29万张图片，其中21万张图片作为训练集（带标注），8万张作为测试集（无标注）。数据集采自中国街景，并由街景图片中的文字行区域（例如店铺标牌、地标等等）截取出来而形成。所有图像都经过一些预处理，将文字区域利用仿射变化，等比映射为一张高为48像素的图片，如图所示：
+
+    ![](./images/ch_street_rec_1.png)
+
+    (a) 标注：魅派集成吊顶
+
+    ![](./images/ch_street_rec_2.png)
+    (b) 标注：母婴用品连锁
+- **下载地址**
+<https://aistudio.baidu.com/aistudio/datasetdetail/8429>
+
+#### 4、中文文档文字识别
+
+- **数据来源**：<https://github.com/YCG09/chinese_ocr>
+- **数据简介**：
+    - 共约364万张图片，按照99:1划分成训练集和验证集。
+    - 数据利用中文语料库（新闻 + 文言文），通过字体、大小、灰度、模糊、透视、拉伸等变化随机生成
+    - 包含汉字、英文字母、数字和标点共5990个字符（字符集合：<https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt> ）
+    - 每个样本固定10个字符，字符随机截取自语料库中的句子
+    - 图片分辨率统一为280x32
+
+      ![](./images/ch_doc1.jpg)
+
+      ![](./images/ch_doc3.jpg)
+- **下载地址**：<https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw> (密码：lu7m)
+
+#### 5、ICDAR2019-ArT
+
+- **数据来源**：<https://ai.baidu.com/broad/introduction?dataset=art>
+- **数据简介**：共包含10,166张图像，训练集5603图，测试集4563图。由Total-Text、SCUT-CTW1500、Baidu Curved Scene Text (ICDAR2019-LSVT部分弯曲数据) 三部分组成，包含水平、多方向和弯曲等多种形状的文本。
+    ![](./images/ArT.jpg)
+- **下载地址**：<https://ai.baidu.com/broad/download?dataset=art>
+
+#### 6、电子印章数据集
+
+- **数据来源**：<https://aistudio.baidu.com/aistudio/datasetdetail/154271/0>
+- **数据简介**：共包含10000张图像，训练集8000图，测试集2000图。数据集是用程序合成的，并不涉及隐私安全，主要用于印章弯曲文本的训练与检测。由开发者[jingsongliujing](https://github.com/jingsongliujing)贡献
+- **下载地址**：<https://aistudio.baidu.com/aistudio/datasetdetail/154271/0>
+
+## 参考文献
+
+**ICDAR 2019-LSVT Challenge**
+
+```bibtex
+@article{sun2019icdar,
+  title={ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling--RRC-LSVT},
+  author={Sun, Yipeng and Ni, Zihan and Chng, Chee-Kheng and Liu, Yuliang and Luo, Canjie and Ng, Chun Chet and Han, Junyu and Ding, Errui and Liu, Jingtuo and Karatzas, Dimosthenis and others},
+  journal={arXiv preprint arXiv:1909.07741},
+  year={2019}
+}
+```
+
+**ICDAR 2019-ArT Challenge**
+
+```bibtex
+@article{chng2019icdar2019,
+  title={ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)},
+  author={Chng, Chee-Kheng and Liu, Yuliang and Sun, Yipeng and Ng, Chun Chet and Luo, Canjie and Ni, Zihan and Fang, ChuanMing and Zhang, Shuaitao and Han, Junyu and Ding, Errui and others},
+  journal={arXiv preprint arXiv:1909.07145},
+  year={2019}
+}
+```

docs/datasets/handwritten_datasets.en.md

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+---
+comments: true
+---
+
+
+# Handwritten OCR dataset
+
+Here we have sorted out the commonly used handwritten OCR dataset datasets, which are being updated continuously. We welcome you to contribute datasets ~
+
+- [Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset](#Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset)
+- [NIST handwritten single character dataset - English](#NIST handwritten single character dataset - English)
+
+## Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset
+
+- **Data source**: <http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html>
+- **Data introduction**:
+    - It includes online and offline handwritten data,`HWDB1.0~1.2` has totally 3895135 handwritten single character samples, which belong to 7356 categories (7185 Chinese characters and 171 English letters, numbers and symbols);`HWDB2.0~2.2` has totally 5091 pages of images, which are divided into 52230 text lines and 1349414 words. All text and text samples are stored as grayscale images. Some sample words are shown below.
+
+      ![](./images/CASIA_0.jpg)
+
+- **Download address**:<http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html>
+- **使用建议**:Data for single character, white background, can form a large number of text lines for training. White background can be processed into transparent state, which is convenient to add various backgrounds. For the case of semantic needs, it is suggested to extract single character from real corpus to form text lines.
+
+## NIST handwritten single character dataset - English(NIST Handprinted Forms and Characters Database)
+
+- **Data source**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
+- **Data introduction**: NIST19 dataset is suitable for handwritten document and character recognition model training. It is extracted from the handwritten sample form of 3600 authors and contains 810000 character images in total. Nine of them are shown below.
+
+    ![](./images/nist_demo.png)
+
+- **Download address**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)

docs/datasets/handwritten_datasets.md

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+---
+comments: true
+---
+
+
+# 手写OCR数据集
+
+这里整理了常用手写数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+## 中科院自动化研究所-手写中文数据集
+
+- **数据来源**：<http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html>
+- **数据简介**：
+    - 包含在线和离线两类手写数据，`HWDB1.0~1.2`总共有3895135个手写单字样本，分属7356类（7185个汉字和171个英文字母、数字、符号）；`HWDB2.0~2.2`总共有5091页图像，分割为52230个文本行和1349414个文字。所有文字和文本样本均存为灰度图像。部分单字样本图片如下所示。
+
+    ![](./images/CASIA_0.jpg)
+
+- **下载地址**：<http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html>
+- **使用建议**：数据为单字，白色背景，可以大量合成文字行进行训练。白色背景可以处理成透明状态，方便添加各种背景。对于需要语义的情况，建议从真实语料出发，抽取单字组成文字行
+
+## NIST手写单字数据集-英文(NIST Handprinted Forms and Characters Database)
+
+- **数据来源**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
+- **数据简介**: NIST19数据集适用于手写文档和字符识别的模型训练，从3600位作者的手写样本表格中提取得到，总共包含81万张字符图片。其中9张图片示例如下：
+
+    ![](./images/nist_demo.png)
+
+- **下载地址**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)

docs/datasets/kie_datasets.en.md

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+---
+comments: true
+---
+
+
+## Key Information Extraction dataset
+
+Here are the common datasets key information extraction, which are being updated continuously. Welcome to contribute datasets.
+
+### 1. FUNSD dataset
+
+- **Data source**: <https://guillaumejaume.github.io/FUNSD/>
+- **Data Introduction**: The FUNSD dataset is a dataset for form comprehension. It contains 199 real, fully annotated scanned images, including market reports, advertisements, and academic reports, etc., and is divided into 149 training set and 50 test set. The FUNSD dataset is suitable for many types of DocVQA tasks, such as field-level entity classification, field-level entity connection, etc. Part of the image and the annotation box visualization are shown below:
+
+    ![](./images/funsd_demo/gt_train_00040534.jpg)
+
+    ![](./images/funsd_demo/gt_train_00070353.jpg)
+
+    In the figure, the orange area represents `header`, the light blue area represents `question`, the green area represents `answer`, and the pink area represents `other`.
+
+- **Download address**: <https://guillaumejaume.github.io/FUNSD/download/>
+
+### 2. XFUND dataset
+
+- **Data source**: <https://github.com/doc-analysis/XFUND>
+- **Data introduction**: XFUND is a multilingual form comprehension dataset, which contains form data in 7 different languages, and all are manually annotated in the form of key-value pairs. The data for each language contains 199 form data, which are divided into 149 training sets and 50 test sets. Part of the image and the annotation box visualization are shown below.
+
+    ![](./images/xfund_demo/gt_zh_train_0.jpg)
+
+    ![](./images/xfund_demo/gt_zh_train_1.jpg)
+
+- **Download address**: <https://github.com/doc-analysis/XFUND/releases/tag/v1.0>
+
+### 3. wildreceipt dataset
+
+- **Data source**: <https://arxiv.org/abs/2103.14470>
+- **Data introduction**: wildreceipt is an English receipt dataset, which contains 26 different categories. There are 1267 training images and 472 evaluation images, in which 50,000 textlines and boxes are annotated. Part of the image and the annotation box visualization are shown below.
+
+    ![](./images/wildreceipt_demo/2769.jpeg)
+
+    ![](./images/wildreceipt_demo/1bbe854b8817dedb8585e0732089fd1f752d2cec.jpeg)
+
+**Note：** Boxes with category `Ignore` or `Others` are not visualized here.
+
+- **Download address**：
+    - Official dataset: [link](https://download.openmmlab.com/mmocr/data/wildreceipt.tar)
+    - Dataset converted for PaddleOCR training process: [link](https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar)

docs/datasets/kie_datasets.md

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+---
+comments: true
+---
+
+
+# 关键信息抽取数据集
+
+这里整理了常见的关键信息抽取数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+## 1. FUNSD数据集
+
+- **数据来源**：<https://guillaumejaume.github.io/FUNSD/>
+- **数据简介**：FUNSD数据集是一个用于表单理解的数据集，它包含199张真实的、完全标注的扫描版图片，类型包括市场报告、广告以及学术报告等，并分为149张训练集以及50张测试集。FUNSD数据集适用于多种类型的DocVQA任务，如字段级实体分类、字段级实体连接等。部分图像以及标注框可视化如下所示:
+
+<div align="center">
+    <img src="./images/funsd_demo/gt_train_00040534.jpg" width="500">
+    <img src="./images/funsd_demo/gt_train_00070353.jpg" width="500">
+</div>
+    图中，橙色区域代表`header`，淡蓝色区域代表`question`, 绿色区域表`answer`，粉红色代区域表`other`。
+
+- **下载地址**：<https://guillaumejaume.github.io/FUNSD/download/>
+
+## 2. XFUND数据集
+
+- **数据来源**：<https://github.com/doc-analysis/XFUND>
+- **数据简介**：XFUND是一个多语种表单理解数据集，它包含7种不同语种的表单数据，并且全部用人工进行了键-值对形式的标注。其中每个语种的数据都包含了199张表单数据，并分为149张训练集以及50张测试集。部分图像以及标注框可视化如下所示:
+
+<div align="center">
+    <img src="./images/xfund_demo/gt_zh_train_0.jpg" width="500">
+    <img src="./images/xfund_demo/gt_zh_train_1.jpg" width="500">
+</div>
+
+- **下载地址**：<https://github.com/doc-analysis/XFUND/releases/tag/v1.0>
+
+## 3. wildreceipt数据集
+
+- **数据来源**：<https://arxiv.org/abs/2103.14470>
+- **数据简介**：wildreceipt数据集是英文发票数据集，包含26个类别（此处类别体系包含`Ignore`类别），共标注了50000个文本框。其中训练集包含1267张图片，测试集包含472张图片。部分图像以及标注框可视化如下所示:
+
+<div align="center">
+    <img src="./images/wildreceipt_demo/2769.jpeg" width="500">
+    <img src="./images/wildreceipt_demo/1bbe854b8817dedb8585e0732089fd1f752d2cec.jpeg" width="500">
+</div>
+
+**注：** 这里对于类别为`Ignore`或者`Others`的文本，没有进行可视化。
+
+- **下载地址**：
+    - 原始数据下载地址：[链接](https://download.openmmlab.com/mmocr/data/wildreceipt.tar)
+    - 数据格式转换后适配于PaddleOCR训练的数据下载地址：[链接](https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar)

docs/datasets/layout_datasets.en.md

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+---
+comments: true
+---
+
+
+## Layout Analysis Dataset
+
+Here are the common datasets of layout analysis, which are being updated continuously. Welcome to contribute datasets.
+
+Most of the layout analysis datasets are object detection datasets. In addition to open source datasets, you can also label or synthesize datasets using tools such as [labelme](https://github.com/wkentaro/labelme) and so on.
+
+### 1. PubLayNet dataset
+
+- **Data source**: <https://github.com/ibm-aur-nlp/PubLayNet>
+- **Data introduction**: The PubLayNet dataset contains 350000 training images and 11000 validation images. There are 5 categories in total, namely: `text, title, list, table, figure`. Some images and their annotations as shown below.
+
+    ![](./images/publaynet_demo/gt_PMC3724501_00006.jpg)
+
+    ![](./images/publaynet_demo/gt_PMC5086060_00002.jpg)
+
+- **Download address**: <https://developer.ibm.com/exchanges/data/all/publaynet/>
+- **Note**: When using this dataset, you need to follow [CDLA-Permissive](https://cdla.io/permissive-1-0/) license.
+
+### 2、CDLA dataset
+
+- **Data source**: <https://github.com/buptlihang/CDLA>
+- **Data introduction**: CDLA dataset contains 5000 training images and 1000 validation images with 10 categories, which are `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`. Some images and their annotations as shown below.
+
+    ![](./images/CDLA_demo/val_0633.jpg)
+
+    ![](./images/CDLA_demo/val_0941.jpg)
+
+- **Download address**: <https://github.com/buptlihang/CDLA>
+- **Note**: When you train detection model on CDLA dataset using [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/develop), you need to remove the label `__ignore__` and `_background_`.
+
+### 3、TableBank dataset
+
+- **Data source**: <https://doc-analysis.github.io/tablebank-page/index.html>
+- **Data introduction**: TableBank dataset contains 2 types of document: Latex (187199 training images, 7265 validation images and 5719 testing images) and Word (73383 training images 2735 validation images and 2281 testing images). Some images and their annotations as shown below.
+
+    ![](./images/tablebank_demo/004.png)
+
+    ![](./images/tablebank_demo/005.png)
+
+- **Data source**: <https://doc-analysis.github.io/tablebank-page/index.html>
+- **Note**: When using this dataset, you need to follow [Apache-2.0](https://github.com/doc-analysis/TableBank/blob/master/LICENSE) license.

docs/datasets/layout_datasets.md

@@ -0,0 +1,49 @@
+---
+comments: true
+---
+
+
+## 版面分析数据集
+
+这里整理了常用版面分析数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+版面分析数据集多为目标检测数据集，除了开源数据，用户还可使用合成工具自行合成，如[labelme](https://github.com/wkentaro/labelme)等。
+
+### 1、publaynet数据集
+
+- **数据来源**：<https://github.com/ibm-aur-nlp/PubLayNet>
+- **数据简介**：publaynet数据集的训练集合中包含35万张图像，验证集合中包含1.1万张图像。总共包含5个类别，分别是： `text, title, list, table, figure`。部分图像以及标注框可视化如下所示。
+
+<div align="center">
+    <img src="./images/publaynet_demo/gt_PMC3724501_00006.jpg" width="500">
+    <img src="./images/publaynet_demo/gt_PMC5086060_00002.jpg" width="500">
+</div>
+
+- **下载地址**：<https://developer.ibm.com/exchanges/data/all/publaynet/>
+- **说明**：使用该数据集时，需要遵守[CDLA-Permissive](https://cdla.io/permissive-1-0/)协议。
+
+### 2、CDLA数据集
+
+- **数据来源**：<https://github.com/buptlihang/CDLA>
+- **数据简介**：CDLA据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
+
+<div align="center">
+    <img src="./images/CDLA_demo/val_0633.jpg" width="500">
+    <img src="./images/CDLA_demo/val_0941.jpg" width="500">
+</div>
+
+- **下载地址**：<https://github.com/buptlihang/CDLA>
+- **说明**：基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/develop)套件，在该数据集上训练目标检测模型时，在转换label时，需要将`label.txt`中的`__ignore__`与`_background_`去除。
+
+### 3、TableBank数据集
+
+- **数据来源**：<https://doc-analysis.github.io/tablebank-page/index.html>
+- **数据简介**：TableBank数据集包含Latex（训练集187199张，验证集7265张，测试集5719张）与Word（训练集73383张，验证集2735张，测试集2281张）两种类别的文档。仅包含`Table` 1个类别。部分图像以及标注框可视化如下所示。
+
+<div align="center">
+    <img src="./images/tablebank_demo/004.png" height="700">
+    <img src="./images/tablebank_demo/005.png" height="700">
+</div>
+
+- **下载地址**：<https://doc-analysis.github.io/tablebank-page/index.html>
+- **说明**：使用该数据集时，需要遵守[Apache-2.0](https://github.com/doc-analysis/TableBank/blob/master/LICENSE)协议。

docs/datasets/ocr_datasets.en.md

@@ -0,0 +1,154 @@
+---
+comments: true
+---
+
+
+# OCR datasets
+
+Here is a list of public datasets commonly used in OCR, which are being continuously updated. Welcome to contribute datasets!
+
+## 1. Text detection
+
+### 1.1 PaddleOCR text detection format annotation
+
+The annotation file formats supported by the PaddleOCR text detection algorithm are as follows, separated by "\t":
+
+```text linenums="1"
+"Image file name             Image annotation information encoded by json.dumps"
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
+```
+
+The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
+
+The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
+
+`transcription` represents the text of the current text box. **When its content is "###" it means that the text box is invalid and will be skipped during training.**
+
+If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
+
+### 1.2 Public dataset
+
+| dataset | Image download link | PaddleOCR format annotation download link |
+|---|---|---|
+| ICDAR 2015 | <https://rrc.cvc.uab.es/?ch=4&com=downloads>            | [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) |
+| ctw1500 | <https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip>   | Included in the downloaded image zip                                                                                                           |
+| total text | <https://paddleocr.bj.bcebos.com/dataset/total_text.tar> |  Included in the downloaded image zip  |
+
+#### 1.2.1 ICDAR 2015
+
+The icdar2015 dataset contains train set which has 1000 images obtained with wearable cameras and test set which has 500 images obtained with wearable cameras. The icdar2015 dataset can be downloaded from the link in the table above. Registration is required for downloading.
+
+After registering and logging in, download the part marked in the red box in the figure below. And, the content downloaded by `Training Set Images` should be saved as the folder `icdar_c4_train_imgs`, and the content downloaded by `Test Set Images` is saved as the folder `ch4_test_images`
+
+![](./images/ic15_location_download.png)
+
+Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. Then download the PaddleOCR format annotation file from the table above.
+
+PaddleOCR also provides a data format conversion script, which can convert the official website label to the PaddleOCR format. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
+
+```bash linenums="1"
+# Convert the label file downloaded from the official website to train_icdar2015_label.txt
+python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/"  \
+                    --input_path="/path/to/ch4_training_localization_transcription_gt" \
+                    --output_label="/path/to/train_icdar2015_label.txt"
+```
+
+After decompressing the data set and downloading the annotation file, PaddleOCR/train_data/ has two folders and two files, which are:
+
+```text linenums="1"
+/PaddleOCR/train_data/icdar2015/text_localization/
+  └─ icdar_c4_train_imgs/         Training data of icdar dataset
+  └─ ch4_test_images/             Testing data of icdar dataset
+  └─ train_icdar2015_label.txt    Training annotation of icdar dataset
+  └─ test_icdar2015_label.txt     Test annotation of icdar dataset
+```
+
+## 2. Text recognition
+
+### 2.1 PaddleOCR text recognition format annotation
+
+The text recognition algorithm in PaddleOCR supports two data formats:
+
+- `lmdb` is used to train data sets stored in lmdb format, use [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) to load;
+- `common dataset` is used to train data sets stored in text files, use [simple_dataset.py](../../../ppocr/data/simple_dataset.py) to load.
+
+If you want to use your own data for training, please refer to the following to organize your data.
+
+#### Training set
+
+It is recommended to put the training images in the same folder, and use a txt file (rec_gt_train.txt) to store the image path and label. The contents of the txt file are as follows:
+
+- Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error
+
+```text linenums="1"
+" Image file name           Image annotation "
+
+train_data/rec/train/word_001.jpg   简单可依赖
+train_data/rec/train/word_002.jpg   用科技让复杂的世界更简单
+...
+```
+
+The final training set should have the following file structure:
+
+```text linenums="1"
+|-train_data
+  |-rec
+    |- rec_gt_train.txt
+    |- train
+        |- word_001.png
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+```
+
+#### Test set
+
+Similar to the training set, the test set also needs to be provided a folder containing all images (test) and a rec_gt_test.txt. The structure of the test set is as follows:
+
+```text linenums="1"
+|-train_data
+  |-rec
+    |-ic15_data
+        |- rec_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+### 2.2 Public dataset
+
+| dataset | Image download link | PaddleOCR format annotation download link |
+|---|---|---|
+| en benchmark(MJ, SJ, IIIT, SVT, IC03, IC13, IC15, SVTP, and CUTE.) | [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | LMDB format, which can be loaded directly with [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) |
+|ICDAR 2015| <http://rrc.cvc.uab.es/?ch=4&com=downloads> | [train](https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt)/ [test](https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt) |
+| Multilingual datasets |[Baidu network disk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) Extraction code: frgi <br> [google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) | Included in the downloaded image zip |
+
+#### 2.1 ICDAR 2015
+
+The ICDAR 2015 dataset can be downloaded from the link in the table above for quick validation. The lmdb format dataset required by en benchmark can also be downloaded from the table above.
+
+Then download the PaddleOCR format annotation file from the table above.
+
+PaddleOCR also provides a data format conversion script, which can convert the ICDAR official website label to the data format supported by PaddleOCR. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
+
+```bash linenums="1"
+# Convert the label file downloaded from the official website to rec_gt_label.txt
+python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
+```
+
+The data format is as follows, (a) is the original picture, (b) is the Ground Truth text file corresponding to each picture:
+
+![](./images/icdar_rec.png)
+
+## 3. Data storage path
+
+The default storage path for PaddleOCR training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
+
+```bash linenums="1"
+# linux and mac os
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
+# windows
+mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>
+```

docs/datasets/ocr_datasets.md

@@ -0,0 +1,160 @@
+---
+comments: true
+---
+
+
+# OCR数据集
+
+这里整理了OCR中常用的公开数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+## 1. 文本检测
+
+### 1.1 PaddleOCR 文字检测数据格式
+
+PaddleOCR 中的文本检测算法支持的标注文件格式如下，中间用"\t"分隔：
+
+```text linenums="1"
+" 图像文件名                    json.dumps编码的图像标注信息"
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
+```
+
+json.dumps编码前的图像标注信息是包含多个字典的list，字典中的 `points` 表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
+`transcription` 表示当前文本框的文字，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
+
+如果您想在我们未提供的数据集上训练，可以按照上述形式构建标注文件。
+
+### 1.2 公开数据集
+
+| 数据集名称 |图片下载地址| PaddleOCR 标注下载地址 |
+|---|---|---|
+| ICDAR 2015 |<https://rrc.cvc.uab.es/?ch=4&com=downloads>| [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) |
+| ctw1500 |<https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip>| 图片下载地址中已包含 |
+| total text |<https://paddleocr.bj.bcebos.com/dataset/total_text.tar>| 图片下载地址中已包含 |
+| td tr |<https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar>| 图片下载地址中已包含 |
+
+#### 1.2.1 ICDAR 2015
+
+ICDAR 2015 数据集包含1000张训练图像和500张测试图像。ICDAR 2015 数据集可以从上表中链接下载，首次下载需注册。
+注册完成登陆后，下载下图中红色框标出的部分，其中， `Training Set Images`下载的内容保存在`icdar_c4_train_imgs`文件夹下，`Test Set Images` 下载的内容保存早`ch4_test_images`文件夹下
+
+ <img src="./images/ic15_location_download.png" align="middle" width = "700"/>
+
+将下载到的数据集解压到工作目录下，假设解压在 PaddleOCR/train_data/下。然后从上表中下载转换好的标注文件。
+
+PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
+
+```bash linenums="1"
+# 将官网下载的标签文件转换为 train_icdar2015_label.txt
+python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/"  \
+                    --input_path="/path/to/ch4_training_localization_transcription_gt" \
+                    --output_label="/path/to/train_icdar2015_label.txt"
+```
+
+解压数据集和下载标注文件后，PaddleOCR/train_data/ 有两个文件夹和两个文件，按照如下方式组织icdar2015数据集：
+
+```text linenums="1"
+/PaddleOCR/train_data/icdar2015/text_localization/
+  └─ icdar_c4_train_imgs/         icdar 2015 数据集的训练数据
+  └─ ch4_test_images/             icdar 2015 数据集的测试数据
+  └─ train_icdar2015_label.txt    icdar 2015 数据集的训练标注
+  └─ test_icdar2015_label.txt     icdar 2015 数据集的测试标注
+```
+
+## 2. 文本识别
+
+### 2.1 PaddleOCR 文字识别数据格式
+
+PaddleOCR 中的文字识别算法支持两种数据格式:
+
+- `lmdb` 用于训练以lmdb格式存储的数据集，使用 [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) 进行读取;
+- `通用数据` 用于训练以文本文件存储的数据集，使用 [simple_dataset.py](../../../ppocr/data/simple_dataset.py)进行读取。
+
+下面以通用数据集为例， 介绍如何准备数据集：
+
+#### 训练集
+
+建议将训练图片放入同一个文件夹，并用一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下:
+
+**注意：** txt文件中默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错。
+
+```text linenums="1"
+" 图像文件名                 图像标注信息 "
+
+train_data/rec/train/word_001.jpg   简单可依赖
+train_data/rec/train/word_002.jpg   用科技让复杂的世界更简单
+...
+```
+
+最终训练集应有如下文件结构：
+
+```text linenums="1"
+|-train_data
+  |-rec
+    |- rec_gt_train.txt
+    |- train
+        |- word_001.png
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+```
+
+除上述单张图像为一行格式之外，PaddleOCR也支持对离线增广后的数据进行训练，为了防止相同样本在同一个batch中被多次采样，我们可以将相同标签对应的图片路径写在一行中，以列表的形式给出，在训练中，PaddleOCR会随机选择列表中的一张图片进行训练。对应地，标注文件的格式如下：
+
+```text linenums="1"
+["11.jpg", "12.jpg"]   简单可依赖
+["21.jpg", "22.jpg", "23.jpg"]   用科技让复杂的世界更简单
+3.jpg   ocr
+```
+
+上述示例标注文件中，"11.jpg"和"12.jpg"的标签相同，都是`简单可依赖`，在训练的时候，对于该行标注，会随机选择其中的一张图片进行训练。
+
+#### 验证集
+
+同训练集类似，验证集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，验证集的结构如下所示：
+
+```text linenums="1"
+|-train_data
+  |-rec
+    |- rec_gt_test.txt
+    |- test
+        |- word_001.jpg
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+```
+
+### 2.2 公开数据集
+
+| 数据集名称 | 图片下载地址 | PaddleOCR 标注下载地址                                                         |
+|---|---|---------------------------------------------------------------------|
+| en benchmark(MJ, SJ, IIIT, SVT, IC03, IC13, IC15, SVTP, and CUTE.) | [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | LMDB格式，可直接用[lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py)加载 |
+|ICDAR 2015| <http://rrc.cvc.uab.es/?ch=4&com=downloads> | [train](https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt)/ [test](https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt) |
+| 多语言数据集 |[百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 提取码：frgi <br> [google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) | 图片下载地址中已包含 |
+
+#### 2.1 ICDAR 2015
+
+ICDAR 2015 数据集可以在上表中链接下载，用于快速验证。也可以从上表中下载 en benchmark 所需的lmdb格式数据集。
+
+下载完图片后从上表中下载转换好的标注文件。
+
+PaddleOCR 也提供了数据格式转换脚本，可以将ICDAR官网 label 转换为PaddleOCR支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
+
+```bash linenums="1"
+# 将官网下载的标签文件转换为 rec_gt_label.txt
+python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
+```
+
+数据样式格式如下，(a)为原始图片,(b)为每张图片对应的 Ground Truth 文本文件：
+
+![](./images/icdar_rec.png)
+
+## 3. 数据存放路径
+
+PaddleOCR训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
+
+```bash linenums="1"
+# linux and mac os
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
+# windows
+mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>
+```

docs/datasets/table_datasets.en.md

@@ -0,0 +1,40 @@
+---
+comments: true
+---
+
+
+# Table Recognition Datasets
+
+Here are the commonly used table recognition datasets, which are being updated continuously. Welcome to contribute datasets~
+
+## Dataset Summary
+
+| dataset | Image download link | PPOCR format annotation download link |
+|---|---|---|
+| PubTabNet |<https://github.com/ibm-aur-nlp/PubTabNet>| jsonl format, which can be loaded directly with [pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py) |
+| TAL Table Recognition Competition Dataset |<https://ai.100tal.com/dataset>| jsonl format, which can be loaded directly with [pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py) |
+| WTW Chinese scene table dataset |<https://github.com/wangwen-whu/WTW-Dataset>| Conversion is required to load with [pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py)|
+
+## 1. PubTabNet
+
+- **Data Introduction**：The training set of the PubTabNet dataset contains 500,000 images and the validation set contains 9000 images. Part of the image visualization is shown below.
+
+    ![](./images/table_PubTabNet_demo/PMC524509_007_00.png)
+
+    ![](./images/table_PubTabNet_demo/PMC535543_007_01.png)
+
+- **illustrate**：When using this dataset, the [CDLA-Permissive](https://cdla.io/permissive-1-0/) protocol is required.
+
+## 2. TAL Table Recognition Competition Dataset
+
+- **Data Introduction**：The training set of the TAL table recognition competition dataset contains 16,000 images. The validation set does not give trainable annotations.
+
+    ![](./images/table_tal_demo/1.jpg)
+
+    ![](./images/table_tal_demo/2.jpg)
+
+## 3. WTW Chinese scene table dataset
+
+- **Data Introduction**：The WTW Chinese scene table dataset consists of two parts: table detection and table data. The dataset contains images of two scenes, scanned and photographed.
+
+    ![img](./images/20210816_210413.gif)

docs/datasets/table_datasets.md

@@ -0,0 +1,43 @@
+---
+comments: true
+typora-copy-images-to: images
+---
+
+
+# 表格识别数据集
+
+这里整理了常用表格识别数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+## 数据集汇总
+
+| 数据集名称 |图片下载地址| PPOCR标注下载地址 |
+|---|---|---|
+| PubTabNet |<https://github.com/ibm-aur-nlp/PubTabNet>| jsonl格式，可直接用[pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py)加载 |
+| 好未来表格识别竞赛数据集 |<https://ai.100tal.com/dataset>| jsonl格式，可直接用[pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py)加载 |
+| WTW中文场景表格数据集 |<https://github.com/wangwen-whu/WTW-Dataset>| 需要进行转换后才能用[pubtab_dataset.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/data/pubtab_dataset.py)加载 |
+
+## 1. PubTabNet数据集
+
+- **数据简介**：PubTabNet数据集的训练集合中包含50万张图像，验证集合中包含0.9万张图像。部分图像可视化如下所示。
+
+<div align="center">
+    <img src="./images/table_PubTabNet_demo/PMC524509_007_00.png" width="500">
+    <img src="./images/table_PubTabNet_demo/PMC535543_007_01.png" width="500">
+</div>
+
+- **说明**：使用该数据集时，需要遵守[CDLA-Permissive](https://cdla.io/permissive-1-0/)协议。
+
+## 2. 好未来表格识别竞赛数据集
+
+- **数据简介**：好未来表格识别竞赛数据集的训练集合中包含1.6万张图像。验证集未给出可训练的标注。
+
+<div align="center">
+    <img src="./images/table_tal_demo/1.jpg" width="500">
+    <img src="./images/table_tal_demo/2.jpg" width="500">
+</div>
+
+## 3. WTW中文场景表格数据集
+
+- **数据简介**：WTW中文场景表格数据集包含表格检测和表格数据两部分数据，数据集中同时包含扫描和拍照两张场景的图像。
+
+  ![img](./images/20210816_210413.gif)

docs/datasets/vertical_and_multilingual_datasets.en.md

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+---
+comments: true
+---
+
+
+# Vertical multi-language OCR dataset
+
+Here we have sorted out the commonly used vertical multi-language OCR dataset datasets, which are being updated continuously. We welcome you to contribute datasets ～
+
+- [Chinese urban license plate dataset](#Chinese urban license plate dataset)
+- [Bank credit card dataset](#Bank credit card dataset)
+- [Captcha dataset-Captcha](#Captcha dataset-Captcha)
+- [multi-language dataset](#multi-language dataset)
+
+## Chinese urban license plate dataset
+
+- **Data source**：[CCPD](https://github.com/detectRecog/CCPD)
+
+- **Data introduction**: It contains more than 250000 vehicle license plate images and vehicle license plate detection and recognition information labeling. It contains the following license plate image information in different scenes.
+
+    - CCPD-Base: General license plate picture
+    - CCPD-DB: The brightness of license plate area is bright, dark or uneven
+    - CCPD-FN: The license plate is farther or closer to the camera location
+    - CCPD-Rotate: License plate includes rotation (horizontal 20\~50 degrees, vertical-10\~10 degrees)
+    - CCPD-Tilt: License plate includes rotation (horizontal 15\~45 degrees, vertical 15\~45 degrees)
+    - CCPD-Blur: The license plate contains blurring due to camera lens jitter
+    - CCPD-Weather: The license plate is photographed on rainy, snowy or foggy days
+    - CCPD-Challenge: So far, some of the most challenging images in license plate detection and recognition tasks
+    - CCPD-NP: Pictures of new cars without license plates.
+
+      ![](./images/ccpd_demo.png)
+
+- **Download address**
+    - Baidu cloud download address (extracted code is hm0U): [https://pan.baidu.com/s/1i5AOjAbtkwb17Zy-NQGqkw](https://pan.baidu.com/s/1i5AOjAbtkwb17Zy-NQGqkw)
+    - Google drive download address:[https://drive.google.com/file/d/1rdEsCUcIUaYOVRkx5IMTRNA7PcGMmSgc/view](https://drive.google.com/file/d/1rdEsCUcIUaYOVRkx5IMTRNA7PcGMmSgc/view)
+
+## Bank credit card dataset
+
+- **Data source**: [source](https://www.kesci.com/home/dataset/5954cf1372ead054a5e25870)
+
+- **Data introduction**: There are three types of training data
+    - 1.Sample card data of China Merchants Bank: including card image data and annotation data, a total of 618 pictures
+    - 2.Single character data: including pictures and annotation data, 37 pictures in total.
+    - 3.There are only other bank cards, no more detailed information, a total of 50 pictures.
+
+    - The demo image is shown as follows. The annotation information is stored in excel, and the demo image below is marked as
+        - Top 8 card number: 62257583
+        - Card type: card of our bank
+        - End of validity: 07/41
+        - Chinese phonetic alphabet of card users: MICHAEL
+
+          ![](./images/cmb_demo.jpg)
+
+- **Download address**: [cmb2017-2.zip](https://cdn.kesci.com/cmb2017-2.zip)
+
+## Captcha dataset-Captcha
+
+- **Data source**: [captcha](https://github.com/lepture/captcha)
+- **Data introduction**: This is a toolkit for data synthesis. You can output captcha images according to the input text. Use the toolkit to generate several demo images as follows.
+
+    ![](./images/captcha_demo.png)
+
+- **Download address**: The dataset is generated and has no download address.
+
+## multi-language dataset(Multi-lingual scene text detection and recognition)
+
+- **Data source**: [source](https://rrc.cvc.uab.es/?ch=15&com=downloads)
+- **Data introduction**: Multi language detection dataset MLT contains both language recognition and detection tasks.
+    - In the detection task, the training set contains 10000 images in 10 languages, and each language contains 1000 training images. The test set contains 10000 images.
+    - In the recognition task, the training set contains 111998 samples.
+- **Download address**: The training set is large and can be downloaded in two parts. It can only be downloaded after registering on the website:
+[source](https://rrc.cvc.uab.es/?ch=15&com=downloads)

docs/datasets/vertical_and_multilingual_datasets.md

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+---
+comments: true
+---
+
+
+# 垂类多语言OCR数据集
+
+这里整理了常用垂类和多语言OCR数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+
+## 中国城市车牌数据集
+
+- **数据来源**：[CCPD](https://github.com/detectRecog/CCPD)
+- **数据简介**: 包含超过25万张中国城市车牌图片及车牌检测、识别信息的标注。包含以下几种不同场景中的车牌图片信息。
+    - CCPD-Base: 通用车牌图片
+    - CCPD-DB: 车牌区域亮度较亮、较暗或者不均匀
+    - CCPD-FN: 车牌离摄像头拍摄位置相对更远或者更近
+    - CCPD-Rotate: 车牌包含旋转（水平20\~50度，竖直-10\~10度）
+    - CCPD-Tilt: 车牌包含旋转（水平15\~45度，竖直15\~45度）
+    - CCPD-Blur: 车牌包含由于摄像机镜头抖动导致的模糊情况
+    - CCPD-Weather: 车牌在雨天、雪天或者雾天拍摄得到
+    - CCPD-Challenge: 至今在车牌检测识别任务中最有挑战性的一些图片
+    - CCPD-NP: 没有安装车牌的新车图片。
+
+      ![](./images/ccpd_demo.png)
+
+- **下载地址**
+    - 百度云下载地址(提取码是hm0U): [link](https://pan.baidu.com/s/1i5AOjAbtkwb17Zy-NQGqkw)
+    - Google drive下载地址：[link](https://drive.google.com/file/d/1rdEsCUcIUaYOVRkx5IMTRNA7PcGMmSgc/view)
+
+## 银行信用卡数据集
+
+- **数据来源**: [source](https://www.kesci.com/home/dataset/5954cf1372ead054a5e25870)
+
+- **数据简介**: 训练数据共提供了三类数据
+    - 1.招行样卡数据： 包括卡面图片数据及标注数据，总共618张图片
+    - 2.单字符数据： 包括图片及标注数据，总共37张图片。
+    - 3.仅包含其他银行卡面，不具有更细致的信息，总共50张图片。
+
+    - demo图片展示如下，标注信息存储在excel表格中，下面的demo图片标注为
+        - 前8位卡号：62257583
+        - 卡片种类：本行卡
+        - 有效期结束：07/41
+        - 卡用户拼音：MICHAEL
+
+    ![](./images/cmb_demo.jpg)
+
+- **下载地址**: [cmb2017-2.zip](https://cdn.kesci.com/cmb2017-2.zip)
+
+## 验证码数据集-Captcha
+
+- **数据来源**: [captcha](https://github.com/lepture/captcha)
+- **数据简介**: 这是一个数据合成的工具包，可以根据输入的文本，输出验证码图片，使用该工具包生成几张demo图片如下：
+
+    ![](./images/captcha_demo.png)
+
+- **下载地址**: 该数据集是生成得到，无下载地址。
+
+## 多语言数据集(Multi-lingual scene text detection and recognition)
+
+- **数据来源**: [source](https://rrc.cvc.uab.es/?ch=15&com=downloads)
+- **数据简介**: 多语言检测数据集MLT同时包含了语种识别和检测任务。
+    - 在检测任务中，训练集包含10000张图片，共有10种语言，每种语言包含1000张训练图片。测试集包含10000张图片。
+    - 在识别任务中，训练集包含111998个样本。
+- **下载地址**: 训练集较大，分2部分下载，需要在网站上注册之后才能下载：
+[link](https://rrc.cvc.uab.es/?ch=15&com=downloads)