Merge pull request #1 from PaddlePaddle/develop

update-2020-7-17

Merge pull request #1 from PaddlePaddle/develop
update-2020-7-17
26219d5f · shaohua.zhang · GitHub · 0e8a3417 · 311c5997 · 26219d5f
Unverified Commit 26219d5f authored Jul 17, 2020 by shaohua.zhang Committed by GitHub Jul 17, 2020
20 changed files
--- a/.clang_format.hook
+++ b/.clang_format.hook
+#!/bin/bash
+set -e
+readonly VERSION="3.8"
+version=$(clang-format -version)
+if ! [[ $version == *"$VERSION"* ]]; then
+    echo "clang-format version check failed."
+    echo "a version contains '$VERSION' is needed, but get '$version'"
+    echo "you can install the right version, and make an soft-link to '\$PATH' env"
+    exit -1
+fi
+clang-format $@
--- a/.gitignore
+++ b/.gitignore
+# Byte-compiled / optimized / DLL files
+__pycache__/
+.ipynb_checkpoints/
+*.py[cod]
+*$py.class
+# C extensions
+*.so
+inference/
+inference_results/
+output/
+*.DS_Store
+*.vs
+*.user
+*~
+*.vscode
+*.idea
+*.log
+.clang-format
+.clang_format.hook
--- a/README.md
+++ b/README.md
+[English](README_en.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，总模型仅8.6M
+- 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
-## **超轻量级中文OCR体验**
+## 快速体验
-![](doc/imgs_results/11.jpg)
-上图是超轻量级中文OCR模型效果展示，更多效果图请见文末[效果展示](#效果展示)。
-#### 1.环境配置
-请先参考[快速安装](./doc/installation.md)配置PaddleOCR运行环境。
-#### 2.模型下载
-```
+<div align="center">
-# 下载inference模型文件包
+    <img src="doc/imgs_results/11.jpg" width="800">
-wget https://paddleocr.bj.bcebos.com/inference.tar
+</div>
-# inference模型文件包解压
-tar -xf inference.tar
-```
-#### 3.单张图像或者图像集合预测
+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)。
-以下代码实现了文本检测、识别串联推理，在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+- 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。
-# 设置PYTHONPATH环境变量
-export PYTHONPATH=.
-# 预测image_dir指定的单张图像
+<div align="center">
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+<img src="./doc/ocr-android-easyedge.png"  width = "200" height = "200" />
+</div>
-# 预测image_dir指定的图像集合
+- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md)
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
-# 如果想使用CPU进行预测，执行命令如下
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/" --use_gpu=False
-```
-更多的文本检测、识别串联推理使用方式请参考文档教程中[基于预测引擎推理](./doc/inference.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/installation.md)
+- [快速安装](./doc/doc_ch/installation.md)
- [文本检测模型训练/评估/预测](./doc/detection.md)（持续更新中）
+- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
- [文本识别模型训练/评估/预测](./doc/recognition.md)（持续更新中）
+- 算法介绍
- [基于预测引擎推理](./doc/inference.md)（持续更新中）
+    - [文本检测](#文本检测算法)
+    - [文本识别](#文本识别算法)
-## 文本检测算法
+    - [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, comming soon)
+- [ ]  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|Hmean|下载链接|
+|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|
 |-|-|-|-|
-|EAST|ResNet50_vd|85.85%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
+|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)|
-|EAST|MobileNetV3|79.08%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.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)|
-|DB|ResNet50_vd|83.30%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
-|DB|MobileNetV3|73.00%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_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[文本检测模型训练/评估/预测](./doc/detection.md)。
+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)。
-## 文本识别算法
+<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, comming soon)
+- [ ]  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：
@@ -95,82 +138,76 @@ PaddleOCR开源的文本识别算法列表：
 |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)|
-PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识别模型训练/评估/预测](./doc/recognition.md)。
+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)。
-## 端到端OCR算法
+<a name="端到端OCR算法"></a>
- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, comming soon)
+### 3.端到端OCR算法
+- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, coming soon)
-<a name="效果展示"></a>
 ## 效果展示
-![](doc/imgs_results/1.jpg)
-![](doc/imgs_results/7.jpg)
-![](doc/imgs_results/12.jpg)
-![](doc/imgs_results/4.jpg)
-![](doc/imgs_results/6.jpg)
-![](doc/imgs_results/9.jpg)
-![](doc/imgs_results/16.png)
-![](doc/imgs_results/22.jpg)
-## 参考文献
-```
-1. EAST:
-@inproceedings{zhou2017east,
-  title={EAST: an efficient and accurate scene text detector},
-  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
-  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
-  pages={5551--5560},
-  year={2017}
-}
-2. DB:
-@article{liao2019real,
-  title={Real-time Scene Text Detection with Differentiable Binarization},
-  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
-  journal={arXiv preprint arXiv:1911.08947},
-  year={2019}
-}
-3. DTRB:
-@inproceedings{baek2019wrong,
-  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
-  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={4715--4723},
-  year={2019}
-}
-4. SAST:
-@inproceedings{wang2019single,
-  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
-  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
-  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
-  pages={1277--1285},
-  year={2019}
-}
-5. SRN:
-@article{yu2020towards,
-  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
-  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
-  journal={arXiv preprint arXiv:2003.12294},
-  year={2020}
-}
-6. end2end-psl:
-@inproceedings{sun2019chinese,
-  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
-  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={9086--9095},
-  year={2019}
-}
-```
+<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。
-## 版本更新
+<a name="贡献代码"></a>
+## 贡献代码
-## 如何贡献代码
 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
--- a/README_en.md
+++ b/README_en.md
+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.
+**Recent updates**、
+- 2020.7.9 Add recognition model to support space, [recognition result](#space Chinese OCR results). For more information: [Recognition](./doc/doc_ch/recognition.md) and [quickstart](./doc/doc_ch/quickstart.md)
+- 2020.7.9 Add data auguments and learning rate decay strategies,please read [config](./doc/doc_en/config_en.md)
+- 2020.6.8 Add [dataset](./doc/doc_en/datasets_en.md) and keep updating
+- 2020.6.5 Support exporting `attention` model to `inference_model`
+- 2020.6.5 Support separate prediction and recognition, output result score
+- [more](./doc/doc_en/update_en.md)
+## FEATURES
+- 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
+<a name="Supported-Chinese-model-list"></a>
+### Supported Chinese models list:
+|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
+|-|-|-|-|-|
+|chinese_db_crnn_mobile|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)
+For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/ocr
+**You can also quickly experience the lightweight Chinese OCR and General Chinese OCR models as follows:**
+## **LIGHTWEIGHT CHINESE OCR AND GENERAL CHINESE OCR INFERENCE**
+![](doc/imgs_results/11.jpg)
+The picture above is the result of our lightweight Chinese OCR model. For more testing results, please see the end of the article [lightweight Chinese OCR results](#lightweight-Chinese-OCR-results) , [General Chinese OCR results](#General-Chinese-OCR-results) and [Support for space Recognition Model](#Space-Chinese-OCR-results).
+#### 1. ENVIRONMENT CONFIGURATION
+Please see [Quick installation](./doc/doc_en/installation_en.md)
+#### 2. DOWNLOAD INFERENCE MODELS
+#### (1) Download lightweight Chinese OCR models
+*If wget is not installed in the windows system, you can copy the link to the browser to download the model. After model downloaded, unzip it and place it in the corresponding directory*
+Copy the detection and recognition 'inference model' address in [Chinese model List](#Supported-Chinese-model-list), download and unpack:
+```
+mkdir inference && cd inference
+# Download the detection part of the Chinese OCR and decompress it
+wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
+# Download the recognition part of the Chinese OCR and decompress it
+wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
+cd ..
+```
+Take lightweight Chinese OCR model as an example:
+```
+mkdir inference && cd inference
+# Download the detection part of the lightweight Chinese OCR and decompress it
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+# Download the recognition part of the lightweight Chinese OCR and decompress it
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+# Download the space-recognized part of the lightweight Chinese OCR and decompress it
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar && tar xf ch_rec_mv3_crnn_enhance_infer.tar
+cd ..
+```
+After the decompression is completed, the file structure should be as follows:
+```
+|-inference
+    |-ch_rec_mv3_crnn
+        |- model
+        |- params
+    |-ch_det_mv3_db
+        |- model
+        |- params
+    ...
+```
+#### 3. SINGLE IMAGE AND BATCH PREDICTION
+The following code implements text detection and recognition inference tandemly. When performing prediction, you need to specify the path of a single image or image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detection model, and the parameter `rec_model_dir` specifies the path to the recognition model. The visual prediction results are saved to the `./inference_results` folder by default.
+```bash
+# Prediction on a single image by specifying image path to image_dir
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+# Prediction on a batch of images by specifying image folder path to image_dir
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+# If you want to use CPU for prediction, you need to set the use_gpu parameter to False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
+```
+To run inference of the Generic Chinese OCR model, follow these steps above to download the corresponding models and update the relevant parameters. Examples are as follows:
+```
+# Prediction on a single image by specifying image path to image_dir
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
+```
+To run inference of the space-Generic Chinese OCR model, follow these steps above to download the corresponding models and update the relevant parameters. Examples are as follows:
+```
+# Prediction on a single image by specifying image path to image_dir
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/"
+```
+For more text detection and recognition models, please refer to the document [Inference](./doc/doc_en/inference_en.md)
+## DOCUMENTATION
+- [Quick installation](./doc/doc_en/installation_en.md)
+- [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
+- [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
+- [Inference](./doc/doc_en/inference_en.md)
+- [Introduction of yml file](./doc/doc_en/config_en.md)
+- [Dataset](./doc/doc_en/datasets_en.md)
+- [FAQ]((#FAQ)
+## 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|
+|-|-|-|-|
+|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)
+## 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|
+|-|-|-|-|
+|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)
+## END-TO-END OCR ALGORITHM
+- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, comming soon)
+<a name="lightweight-Chinese-OCR-results"></a>
+## LIGHTWEIGHT CHINESE OCR RESULTS
+![](doc/imgs_results/1.jpg)
+![](doc/imgs_results/7.jpg)
+![](doc/imgs_results/12.jpg)
+![](doc/imgs_results/4.jpg)
+![](doc/imgs_results/6.jpg)
+![](doc/imgs_results/9.jpg)
+![](doc/imgs_results/16.png)
+![](doc/imgs_results/22.jpg)
+<a name="General-Chinese-OCR-results"></a>
+## General Chinese OCR results
+![](doc/imgs_results/chinese_db_crnn_server/11.jpg)
+![](doc/imgs_results/chinese_db_crnn_server/2.jpg)
+![](doc/imgs_results/chinese_db_crnn_server/8.jpg)
+<a name="Space-Chinese-OCR-results"></a>
+## space Chinese OCR results
+### LIGHTWEIGHT CHINESE OCR RESULTS
+![](doc/imgs_results/img_11.jpg)
+### General Chinese OCR results
+![](doc/imgs_results/chinese_db_crnn_server/en_paper.jpg)
+<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)
+## WELCOME TO THE PaddleOCR TECHNICAL EXCHANGE GROUP
+WeChat: paddlehelp, note OCR, our assistant will get you into the group~
+<img src="./doc/paddlehelp.jpg"  width = "200" height = "200" />
+## REFERENCES
+```
+1. EAST:
+@inproceedings{zhou2017east,
+  title={EAST: an efficient and accurate scene text detector},
+  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
+  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  pages={5551--5560},
+  year={2017}
+}
+2. DB:
+@article{liao2019real,
+  title={Real-time Scene Text Detection with Differentiable Binarization},
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  journal={arXiv preprint arXiv:1911.08947},
+  year={2019}
+}
+3. DTRB:
+@inproceedings{baek2019wrong,
+  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
+  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={4715--4723},
+  year={2019}
+}
+4. SAST:
+@inproceedings{wang2019single,
+  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
+  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
+  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+  pages={1277--1285},
+  year={2019}
+}
+5. SRN:
+@article{yu2020towards,
+  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
+  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
+  journal={arXiv preprint arXiv:2003.12294},
+  year={2020}
+}
+6. end2end-psl:
+@inproceedings{sun2019chinese,
+  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
+  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={9086--9095},
+  year={2019}
+}
+```
+## LICENSE
+This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</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.
--- a/configs/det/det_mv3_db.yml
+++ b/configs/det/det_mv3_db.yml
@@ -6,7 +6,8 @@ Global:
  print_batch_step: 2
  save_model_dir: ./output/det_db/
  save_epoch_step: 200
-  eval_batch_step: 5000
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
  train_batch_size_per_card: 16
  test_batch_size_per_card: 16
  image_shape: [3, 640, 640]
@@ -50,4 +51,4 @@ PostProcess:
  thresh: 0.3
  box_thresh: 0.7
  max_candidates: 1000
-  unclip_ratio: 1.5
+  unclip_ratio: 2.0
\ No newline at end of file
--- a/configs/det/det_mv3_east.yml
+++ b/configs/det/det_mv3_east.yml
@@ -6,7 +6,7 @@ Global:
  print_batch_step: 5
  save_model_dir: ./output/det_east/
  save_epoch_step: 200
-  eval_batch_step: 5000
+  eval_batch_step: [5000, 5000]
  train_batch_size_per_card: 16
  test_batch_size_per_card: 16
  image_shape: [3, 512, 512]

--- a/configs/det/det_r50_vd_db.yml
+++ b/configs/det/det_r50_vd_db.yml
@@ -6,7 +6,7 @@ Global:
  print_batch_step: 2
  save_model_dir: ./output/det_db/
  save_epoch_step: 200
-  eval_batch_step: 5000
+  eval_batch_step: [5000, 5000]
  train_batch_size_per_card: 8
  test_batch_size_per_card: 16
  image_shape: [3, 640, 640]

--- a/configs/det/det_r50_vd_east.yml
+++ b/configs/det/det_r50_vd_east.yml
@@ -6,7 +6,7 @@ Global:
  print_batch_step: 5
  save_model_dir: ./output/det_east/
  save_epoch_step: 200
-  eval_batch_step: 5000
+  eval_batch_step: [5000, 5000]
  train_batch_size_per_card: 8
  test_batch_size_per_card: 16
  image_shape: [3, 512, 512]

--- a/configs/rec/rec_benchmark_reader.yml
+++ b/configs/rec/rec_benchmark_reader.yml
@@ -10,4 +10,3 @@ EvalReader:
 TestReader:
  reader_function: ppocr.data.rec.dataset_traversal,LMDBReader
  lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/
-  infer_img: ./infer_img
\ No newline at end of file
--- a/configs/rec/rec_chinese_common_train.yml
+++ b/configs/rec/rec_chinese_common_train.yml
+Global:
+  algorithm: CRNN
+  use_gpu: true
+  epoch_num: 3000
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_CRNN
+  save_epoch_step: 3
+  eval_batch_step: 2000
+  train_batch_size_per_card: 128
+  test_batch_size_per_card: 128
+  image_shape: [3, 32, 320]
+  max_text_length: 25
+  character_type: ch
+  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
+  loss_type: ctc
+  distort: false
+  use_space_char: false
+  reader_yml: ./configs/rec/rec_chinese_reader.yml
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
+  infer_img:
+Architecture:
+  function: ppocr.modeling.architectures.rec_model,RecModel
+Backbone:
+  function: ppocr.modeling.backbones.rec_resnet_vd,ResNet
+  layers: 34
+Head:
+  function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
+  encoder_type: rnn
+  SeqRNN:
+    hidden_size: 256
+Loss:
+  function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
+Optimizer:
+  function: ppocr.optimizer,AdamDecay
+  base_lr: 0.0005
+  beta1: 0.9
+  beta2: 0.999
--- a/configs/rec/rec_chinese_lite_train.yml
+++ b/configs/rec/rec_chinese_lite_train.yml
@@ -14,10 +14,14 @@ Global:
  character_type: ch
  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
  loss_type: ctc
+  distort: false
+  use_space_char: false
  reader_yml: ./configs/rec/rec_chinese_reader.yml
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

--- a/configs/rec/rec_chinese_reader.yml
+++ b/configs/rec/rec_chinese_reader.yml
@@ -11,4 +11,3 @@ EvalReader:
 TestReader:
  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
-  infer_img: ./infer_img
--- a/configs/rec/rec_icdar15_reader.yml
+++ b/configs/rec/rec_icdar15_reader.yml
@@ -11,4 +11,3 @@ EvalReader:
 TestReader:
  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
-  infer_img: ./infer_img
--- a/configs/rec/rec_icdar15_train.yml
+++ b/configs/rec/rec_icdar15_train.yml
@@ -13,10 +13,14 @@ Global:
  max_text_length: 25
  character_type: en
  loss_type: ctc
+  distort: true
+  debug: false
  reader_yml: ./configs/rec/rec_icdar15_reader.yml
  pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel
@@ -39,3 +43,7 @@ Optimizer:
  base_lr: 0.0005
  beta1: 0.9
  beta2: 0.999
+  decay:
+    function: cosine_decay
+    step_each_epoch: 20
+    total_epoch: 1000
--- a/configs/rec/rec_mv3_none_bilstm_ctc.yml
+++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml
@@ -17,6 +17,7 @@ Global:
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

--- a/configs/rec/rec_mv3_none_none_ctc.yml
+++ b/configs/rec/rec_mv3_none_none_ctc.yml
@@ -17,6 +17,7 @@ Global:
  pretrain_weights: 
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

--- a/configs/rec/rec_mv3_tps_bilstm_attn.yml
+++ b/configs/rec/rec_mv3_tps_bilstm_attn.yml
@@ -13,10 +13,13 @@ Global:
  max_text_length: 25
  character_type: en
  loss_type: attention
+  tps: true
  reader_yml: ./configs/rec/rec_benchmark_reader.yml
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

--- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml
+++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml
@@ -13,10 +13,12 @@ Global:
  max_text_length: 25
  character_type: en
  loss_type: ctc
+  tps: true
  reader_yml: ./configs/rec/rec_benchmark_reader.yml
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:

--- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml
+++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml
@@ -17,6 +17,8 @@ Global:
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

--- a/configs/rec/rec_r34_vd_none_none_ctc.yml
+++ b/configs/rec/rec_r34_vd_none_none_ctc.yml
@@ -17,6 +17,7 @@ Global:
  pretrain_weights:
  checkpoints:
  save_inference_dir:
+  infer_img:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel