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README.md
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[English](README_en.md) | 简体中文 English | [简体中文](README_cn.md)
## 简介 ## INTRODUCTION
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。 PaddleOCR aims to create a rich, leading, and practical OCR tools that help users train better models and apply them into practice.
**直播预告：2020年7月21日晚8点B站直播，PaddleOCR开源大礼包全面解读，直播地址当天更新** **Live stream on coming day**: July 21, 2020 at 8 pm BiliBili station live stream
**近期更新** **Recent updates**
- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
- 2020.7.9 添加支持空格的识别模型，识别效果，预测及训练方式请参考快速开始和文本识别训练相关文档
- 2020.7.9 添加数据增强、学习率衰减策略,具体参考[配置文件](./doc/doc_ch/config.md)
- [more](./doc/doc_ch/update.md)
- 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
- 2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addtion, the benchmarks of the ultra-lightweight Chinese OCR model are provided.
- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
- 2020.7.9 Add a new model to support recognize the character "space".
- 2020.7.9 Add the data augument and learning rate decay strategies during training.
- [more](./doc/doc_en/update_en.md)
## 特性 ## FEATURES
- 超轻量级中文OCR模型，总模型仅8.6M - Ultra-lightweight Chinese OCR model, total model size is only 8.6M
- 单模型支持中英文数字组合识别、竖排文本识别、长文本识别 - Single model supports Chinese and English numbers combination recognition, vertical text recognition, long text recognition
- 检测模型DB（4.1M）+识别模型CRNN（4.5M） - Detection model DB (4.1M) + recognition model CRNN (4.5M)
- 实用通用中文OCR模型 - Various text detection algorithms: EAST, DB
- 多种预测推理部署方案，包括服务部署和端侧部署 - Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
- 多种文本检测训练算法，EAST、DB - Support Linux, Windows, MacOS and other systems.
- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE
- 可运行于Linux、Windows、MacOS等多种系统
## 快速体验 ## Visualization
<div align="center"> ![](doc/imgs_results/11.jpg)
<img src="doc/imgs_results/11.jpg" width="800">
</div>
上图是超轻量级中文OCR模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。 [More visualization](./doc/doc_en/visualization_en.md)
- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr You can also quickly experience the ultra-lightweight Chinese OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)：[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Android手机也可以扫描下面二维码安装体验。 Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in the website to obtain the QR code for installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Also, you can scan the QR code blow to install the App (**Android support only**)
<div align="center"> <div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" /> <img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div> </div>
- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md) - [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
<a name="Supported-Chinese-model-list"></a>
## 中文OCR模型列表 ### Supported Chinese Models:
|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址| |Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
|-|-|-|-|-| |-|-|-|-|-|
|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar) |chinese_db_crnn_mobile|ultra-lightweight Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar) |chinese_db_crnn_server|General Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
## 文档教程
- [快速安装](./doc/doc_ch/installation.md) ## Tutorials
- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md) - [Installation](./doc/doc_en/installation_en.md)
- 算法介绍 - [Quick Start](./doc/doc_en/quickstart_en.md)
- [文本检测](#文本检测算法) - Algorithm introduction
- [文本识别](#文本识别算法) - [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
- [端到端OCR](#端到端OCR算法) - [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
- 模型训练/评估 - [END-TO-END OCR Algorithm](#ENDENDOCRALGORITHM)
- [文本检测](./doc/doc_ch/detection.md) - Model training/evaluation
- [文本识别](./doc/doc_ch/recognition.md) - [Text Detection](./doc/doc_en/detection_en.md)
- [yml参数配置文件介绍](./doc/doc_ch/config.md) - [Text Recognition](./doc/doc_en/recognition_en.md)
- [中文OCR训练预测技巧](./doc/doc_ch/tricks.md) - [Yml Configuration](./doc/doc_en/config_en.md)
- [Tricks](./doc/doc_en/tricks_en.md)
- 预测部署 - 预测部署
- [基于Python预测引擎推理](./doc/doc_ch/inference.md) - [Python Inference](./doc/doc_en/inference_en.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) - [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [服务化部署](./doc/doc_ch/serving.md) - [Serving](./doc/doc_en/serving_en.md)
- [端侧部署](./deploy/lite/readme.md) - [Mobile](./deploy/lite/readme_en.md)
- 模型量化压缩（coming soon） - Model Quantization and Compression (coming soon)
- [Benchmark](./doc/doc_ch/benchmark.md) - [Benchmark](./doc/doc_en/benchmark_en.md)
- 数据集 - 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md) - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md) - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md) - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
- [常用数据标注工具](./doc/doc_ch/data_annotation.md) - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
- [常用数据合成工具](./doc/doc_ch/data_synthesis.md) - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- [FAQ](#FAQ) - [FAQ](#FAQ)
- 效果展示 - Visualization
- [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示) - [Ultra-lightweight Chinese/English OCR Visualization](#UCOCRVIS)
- [通用中文OCR效果展示](#通用中文OCR效果展示) - [General Chinese/English OCR Visualization](#GeOCRVIS)
- [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示) - [Chinese/English OCR Visualization (Support Space Recognization )](#SpaceOCRVIS)
- [技术交流群](#欢迎加入PaddleOCR技术交流群) - [COMMUNITY](#Community)
- [参考文献](./doc/doc_ch/reference.md) - [REFERENCES](./doc/doc_en/reference_en.md)
- [许可证书](#许可证书) - [LICENSE](#LICENSE)
- [贡献代码](#贡献代码) - [CONTRIBUTION](#CONTRIBUTION)
<a name="算法介绍"></a> <a name="TEXTDETECTIONALGORITHM"></a>
## 算法介绍 ## Text Detection Algorithm
<a name="文本检测算法"></a>
### 1.文本检测算法 PaddleOCR open source text detection algorithms list:
PaddleOCR开源的文本检测算法列表：
- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) - [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947)) - [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [ ] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研, coming soon) - [ ] SAST([paper](https://arxiv.org/abs/1908.05498))(Baidu Self-Research, comming soon)
在ICDAR2015文本检测公开数据集上，算法效果如下： On the ICDAR2015 dataset, the text detection result is as follows:
|模型|骨干网络|precision|recall|Hmean|下载链接| |Model|Backbone|precision|recall|Hmean|Download link|
|-|-|-|-|-|-| |-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)| |EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)| |EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)| |DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)| |DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下： For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows:
|模型|骨干网络|配置文件|预训练模型| |Model|Backbone|Configuration file|Pre-trained model|
|-|-|-|-| |-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)| |ultra-lightweight Chinese model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)| |General Chinese OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化 * Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。 For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
<a name="文本识别算法"></a> <a name="TEXTRECOGNITIONALGORITHM"></a>
### 2.文本识别算法 ## Text Recognition Algorithm
PaddleOCR开源的文本识别算法列表： PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) - [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) - [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研, coming soon) - [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research, comming soon)
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| |Model|Backbone|Avg Accuracy|Module combination|Download link|
|-|-|-|-|-| |-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)| |Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)| |Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)| |CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)| |CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)| |STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)| |STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)| |RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)| |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下： We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows:
|Model|Backbone|Configuration file|Pre-trained model|
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-| |-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)| |ultra-lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)| |General Chinese OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。 Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
<a name="端到端OCR算法"></a> <a name="ENDENDOCRALGORITHM"></a>
### 3.端到端OCR算法 ## END-TO-END OCR Algorithm
- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, coming soon) - [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, comming soon)
## 效果展示 ## Visualization
<a name="超轻量级中文OCR效果展示"></a> <a name="UCOCRVIS"></a>
### 1.超轻量级中文OCR效果展示 [more](./doc/doc_ch/visualization.md) ### 1.Ultra-lightweight Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center"> <div align="center">
<img src="doc/imgs_results/1.jpg" width="800"> <img src="doc/imgs_results/1.jpg" width="800">
</div> </div>
<a name="通用中文OCR效果展示"></a> <a name="GeOCRVIS"></a>
### 2.通用中文OCR效果展示 [more](./doc/doc_ch/visualization.md) ### 2. General Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center"> <div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800"> <img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div> </div>
<a name="支持空格的中文OCR效果展示"></a> <a name="SpaceOCRVIS"></a>
### 3.支持空格的中文OCR效果展示 [more](./doc/doc_ch/visualization.md) ### 3.Chinese/English OCR Visualization (Space_support) [more](./doc/doc_en/visualization_en.md)
<div align="center"> <div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800"> <img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div> </div>
<a name="FAQ"></a> <a name="FAQ"></a>
## FAQ ## FAQ
1. **转换attention识别模型时报错：KeyError: 'predict'** 1. Error when using attention-based recognition model: KeyError: 'predict'
问题已解，请更新到最新代码。
The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
2. About inference speed
When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
3. Service deployment and mobile deployment
2. **关于推理速度** It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
图片中的文字较多时，预测时间会增，可以使用--rec_batch_num设置更小预测batch num，默认值为30，可以改为10或其他数值。
3. **服务部署与移动端部署** 4. Release time of self-developed algorithm
预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案，欢迎持续关注。
4. **自研算法发布时间** Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
自研算法SAST、SRN、End2End-PSL都将在7-8月陆续发布，敬请期待。
[more](./doc/doc_ch/FAQ.md) [more](./doc/doc_en/FAQ_en.md)
<a name="欢迎加入PaddleOCR技术交流群"></a> <a name="Community"></a>
## 欢迎加入PaddleOCR技术交流群 ## COMMUNITY
请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍 Scan the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
<div align="center"> <div align="center">
<img src="./doc/joinus.jpg" width = "200" height = "200" /> <img src="./doc/joinus.jpg" width = "200" height = "200" />
</div> </div>
<a name="许可证书"></a> <a name="LICENSE"></a>
## 许可证书 ## LICENSE
本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。 This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
<a name="贡献代码"></a> <a name="CONTRIBUTION"></a>
## 贡献代码 ## CONTRIBUTION
我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。 We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 贡献了英文文档。 - Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) for contributing the English documentation.
- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题 - Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码 - Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集 - Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码 - Thanks [authorfu](https://github.com/authorfu) for contributing Android demo and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
README_cn.md 0 → 100644
View file @ 40284a67
[English](README.md) | 简体中文
## 简介
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
**直播预告：2020年7月21日晚8点B站直播，PaddleOCR开源大礼包全面解读，直播地址当天更新**
**近期更新**
- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
- 2020.7.9 添加支持空格的识别模型，识别效果，预测及训练方式请参考快速开始和文本识别训练相关文档
- 2020.7.9 添加数据增强、学习率衰减策略,具体参考[配置文件](./doc/doc_ch/config.md)
- [more](./doc/doc_ch/update.md)
## 特性
- 超轻量级中文OCR模型，总模型仅8.6M
- 单模型支持中英文数字组合识别、竖排文本识别、长文本识别
- 检测模型DB（4.1M）+识别模型CRNN（4.5M）
- 实用通用中文OCR模型
- 多种预测推理部署方案，包括服务部署和端侧部署
- 多种文本检测训练算法，EAST、DB
- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE
- 可运行于Linux、Windows、MacOS等多种系统
## 快速体验
<div align="center">
<img src="doc/imgs_results/11.jpg" width="800">
</div>
上图是超轻量级中文OCR模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)：[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Android手机也可以扫描下面二维码安装体验。
<div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div>
- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md)
## 中文OCR模型列表
|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
|-|-|-|-|-|
|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
## 文档教程
- [快速安装](./doc/doc_ch/installation.md)
- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
- 算法介绍
- [文本检测](#文本检测算法)
- [文本识别](#文本识别算法)
- [端到端OCR](#端到端OCR算法)
- 模型训练/评估
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
- [yml参数配置文件介绍](./doc/doc_ch/config.md)
- [中文OCR训练预测技巧](./doc/doc_ch/tricks.md)
- 预测部署
- [基于Python预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./doc/doc_ch/serving.md)
- [端侧部署](./deploy/lite/readme.md)
- 模型量化压缩（coming soon）
- [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
- [常用数据标注工具](./doc/doc_ch/data_annotation.md)
- [常用数据合成工具](./doc/doc_ch/data_synthesis.md)
- [FAQ](#FAQ)
- 效果展示
- [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)
- [通用中文OCR效果展示](#通用中文OCR效果展示)
- [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
<a name="算法介绍"></a>
## 算法介绍
<a name="文本检测算法"></a>
### 1.文本检测算法
PaddleOCR开源的文本检测算法列表：
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [ ] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研, coming soon)
在ICDAR2015文本检测公开数据集上，算法效果如下：
|模型|骨干网络|precision|recall|Hmean|下载链接|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。
<a name="文本识别算法"></a>
### 2.文本识别算法
PaddleOCR开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研, coming soon)
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。
<a name="端到端OCR算法"></a>
### 3.端到端OCR算法
- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, coming soon)
## 效果展示
<a name="超轻量级中文OCR效果展示"></a>
### 1.超轻量级中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/1.jpg" width="800">
</div>
<a name="通用中文OCR效果展示"></a>
### 2.通用中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div>
<a name="支持空格的中文OCR效果展示"></a>
### 3.支持空格的中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
<a name="FAQ"></a>
## FAQ
1. **转换attention识别模型时报错：KeyError: 'predict'**
问题已解，请更新到最新代码。
2. **关于推理速度**
图片中的文字较多时，预测时间会增，可以使用--rec_batch_num设置更小预测batch num，默认值为30，可以改为10或其他数值。
3. **服务部署与移动端部署**
预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案，欢迎持续关注。
4. **自研算法发布时间**
自研算法SAST、SRN、End2End-PSL都将在7-8月陆续发布，敬请期待。
[more](./doc/doc_ch/FAQ.md)
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍
<div align="center">
<img src="./doc/joinus.jpg" width = "200" height = "200" />
</div>
<a name="许可证书"></a>
## 许可证书
本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
<a name="贡献代码"></a>
## 贡献代码
我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 贡献了英文文档。
- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
README_en.md deleted 100644 → 0
View file @ b8f5e47e
English | [简体中文](README.md)
## INTRODUCTION
PaddleOCR aims to create a rich, leading, and practical OCR tools that help users train better models and apply them into practice.
**Live stream on coming day**: July 21, 2020 at 8 pm BiliBili station live stream
**Recent updates**
- 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
- 2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addtion, the benchmarks of the ultra-lightweight Chinese OCR model are provided.
- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
- 2020.7.9 Add a new model to support recognize the character "space".
- 2020.7.9 Add the data augument and learning rate decay strategies during training.
- [more](./doc/doc_en/update_en.md)
## FEATURES
- Ultra-lightweight Chinese OCR model, total model size is only 8.6M
- Single model supports Chinese and English numbers combination recognition, vertical text recognition, long text recognition
- Detection model DB (4.1M) + recognition model CRNN (4.5M)
- Various text detection algorithms: EAST, DB
- Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
- Support Linux, Windows, MacOS and other systems.
## Visualization
![](doc/imgs_results/11.jpg)
[More visualization](./doc/doc_en/visualization_en.md)
You can also quickly experience the ultra-lightweight Chinese OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in the website to obtain the QR code for installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Also, you can scan the QR code blow to install the App (**Android support only**)
<div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div>
- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
<a name="Supported-Chinese-model-list"></a>
### Supported Chinese Models:
|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
|-|-|-|-|-|
|chinese_db_crnn_mobile|ultra-lightweight Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|General Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
## Tutorials
- [Installation](./doc/doc_en/installation_en.md)
- [Quick Start](./doc/doc_en/quickstart_en.md)
- Algorithm introduction
- [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
- [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
- [END-TO-END OCR Algorithm](#ENDENDOCRALGORITHM)
- Model training/evaluation
- [Text Detection](./doc/doc_en/detection_en.md)
- [Text Recognition](./doc/doc_en/recognition_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md)
- [Tricks](./doc/doc_en/tricks_en.md)
- 预测部署
- [Python Inference](./doc/doc_en/inference_en.md)
- [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./doc/doc_en/serving_en.md)
- [Mobile](./deploy/lite/readme_en.md)
- Model Quantization and Compression (coming soon)
- [Benchmark](./doc/doc_en/benchmark_en.md)
- 数据集
- [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
- [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
- [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
- [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- [FAQ](#FAQ)
- Visualization
- [Ultra-lightweight Chinese/English OCR Visualization](#UCOCRVIS)
- [General Chinese/English OCR Visualization](#GeOCRVIS)
- [Chinese/English OCR Visualization (Support Space Recognization )](#SpaceOCRVIS)
- [COMMUNITY](#Community)
- [REFERENCES](./doc/doc_en/reference_en.md)
- [LICENSE](#LICENSE)
- [CONTRIBUTION](#CONTRIBUTION)
<a name="TEXTDETECTIONALGORITHM"></a>
## Text Detection Algorithm
PaddleOCR open source text detection algorithms list:
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [ ] SAST([paper](https://arxiv.org/abs/1908.05498))(Baidu Self-Research, comming soon)
On the ICDAR2015 dataset, the text detection result is as follows:
|Model|Backbone|precision|recall|Hmean|Download link|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows:
|Model|Backbone|Configuration file|Pre-trained model|
|-|-|-|-|
|ultra-lightweight Chinese model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|General Chinese OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
<a name="TEXTRECOGNITIONALGORITHM"></a>
## Text Recognition Algorithm
PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research, comming soon)
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
|Model|Backbone|Avg Accuracy|Module combination|Download link|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows:
|Model|Backbone|Configuration file|Pre-trained model|
|-|-|-|-|
|ultra-lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)|
|General Chinese OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
<a name="ENDENDOCRALGORITHM"></a>
## END-TO-END OCR Algorithm
- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, comming soon)
## Visualization
<a name="UCOCRVIS"></a>
### 1.Ultra-lightweight Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/1.jpg" width="800">
</div>
<a name="GeOCRVIS"></a>
### 2. General Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div>
<a name="SpaceOCRVIS"></a>
### 3.Chinese/English OCR Visualization (Space_support) [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
<a name="FAQ"></a>
## FAQ
1. Error when using attention-based recognition model: KeyError: 'predict'
The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
2. About inference speed
When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
3. Service deployment and mobile deployment
It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
4. Release time of self-developed algorithm
Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
[more](./doc/doc_en/FAQ_en.md)
<a name="Community"></a>
## COMMUNITY
Scan the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
<div align="center">
<img src="./doc/joinus.jpg" width = "200" height = "200" />
</div>
<a name="LICENSE"></a>
## LICENSE
This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
<a name="CONTRIBUTION"></a>
## CONTRIBUTION
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) for contributing the English documentation.
- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
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