Merge branch 'develop' into develop

README.md

 English | [简体中文](README_ch.md)
 
 ## Introduction
-PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
+PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
 
 **Recent updates**
 - 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
-- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipline](#PP-OCR-Pipline)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
+- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipeline](#PP-OCR-Pipeline)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
 - 2020.9.17 Update the ultra lightweight ppocr_mobile series and general ppocr_server series Chinese and English ocr models, which are comparable to commercial effects. [Model Downloads](#Supported-Chinese-model-list)
-- 2020.9.17 update [English recognition model](./doc/doc_en/models_list_en.md#english-recognition-model) and [Multilingual recognition model](doc/doc_en/models_list_en.md#english-recognition-model), `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
-- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](./doc/doc_en/whl_en.md)
+- 2020.9.17 update [English recognition model](./doc/doc_en/models_list_en.md#english-recognition-model) and [Multilingual recognition model](doc/doc_en/models_list_en.md#english-recognition-model), `English`, `Chinese`, `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
+- 2020.8.24 Support the use of PaddleOCR through whl package installation，please refer  [PaddleOCR Package](./doc/doc_en/whl_en.md)
 - 2020.8.21 Update the replay and PPT of the live lesson at Bilibili on August 18, lesson 2, easy to learn and use OCR tool spree. [Get Address](https://aistudio.baidu.com/aistudio/education/group/info/1519)
 - [more](./doc/doc_en/update_en.md)
 
@@ -32,6 +32,15 @@ PaddleOCR aims to create rich, leading, and practical OCR tools that help users
 
 The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).
 
+<a name="Community"></a>
+## Community
+- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
+
+<div align="center">
+<img src="./doc/joinus.PNG"  width = "200" height = "200" />
+</div>
+
+
 ## Quick Experience
 
 You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
@@ -55,9 +64,14 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
 | Chinese and English ultra-lightweight OCR model (8.1M)       | ch_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |  
 | Chinese and English general OCR model (155.1M)               | ch_ppocr_server_v1.1_xx      | Server            | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |  
 | Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile            | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) |    [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) |
+| French ultra-lightweight OCR model (4.6M)       | french_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | - | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_train.tar)  |
+| German ultra-lightweight OCR model (4.6M)       | german_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | - |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_train.tar) |
+| Korean ultra-lightweight OCR model (5.9M)       | korean_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | - |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_train.tar)|
+| Japan ultra-lightweight OCR model (6.2M)       | japan_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | - |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_train.tar)  |
 
-For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md)
+For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md).
 
+For a new language request, please refer to [Guideline for new language_requests](#language_requests).
 
 ## Tutorials
 - [Installation](./doc/doc_en/installation_en.md)
@@ -66,7 +80,7 @@ For more model downloads (including multiple languages), please refer to [PP-OCR
 - Algorithm introduction
     - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
     - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
-    - [PP-OCR Pipline](#PP-OCR-Pipline)
+    - [PP-OCR Pipeline](#PP-OCR-Pipeline)
 - Model training/evaluation
     - [Text Detection](./doc/doc_en/detection_en.md)
     - [Text Recognition](./doc/doc_en/recognition_en.md)
@@ -88,21 +102,22 @@ For more model downloads (including multiple languages), please refer to [PP-OCR
     - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
     - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
 - [Visualization](#Visualization)
+- [New language requests](#language_requests)
 - [FAQ](./doc/doc_en/FAQ_en.md)
 - [Community](#Community)
 - [References](./doc/doc_en/reference_en.md)
 - [License](#LICENSE)
 - [Contribution](#CONTRIBUTION)
 
-<a name="PP-OCR-Pipline"></a>
+<a name="PP-OCR-Pipeline"></a>
 
-## PP-OCR Pipline
+## PP-OCR Pipeline
 
 <div align="center">
     <img src="./doc/ppocr_framework.png" width="800">
 </div>
 
-PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim). 
+PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
 
 
 
@@ -126,13 +141,24 @@ PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of thr
     <img src="./doc/imgs_results/1112.jpg" width="800">
 </div>
 
-<a name="Community"></a>
-## Community
-Scan the QR code below with your Wechat and completing the questionnaire, you can access to official technical exchange group.
 
-<div align="center">
-<img src="./doc/joinus.PNG"  width = "200" height = "200" />
-</div>
+<a name="language_requests"></a>
+## Guideline for new language requests
+
+If you want to request a new language support, a PR with 2 following files are needed：
+
+1. In folder [ppocr/utils/dict](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/ppocr/utils/dict),
+it is necessary to submit the dict text to this path and name it with `{language}_dict.txt` that contains a list of all characters. Please see the format example from other files in that folder.
+
+2. In folder [ppocr/utils/corpus](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/ppocr/utils/corpus),
+it is necessary to submit the corpus to this path and name it with `{language}_corpus.txt` that contains a list of words in your language.
+Maybe, 50000 words per language is necessary at least.
+Of course, the more, the better.
+
+If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.
+
+More details, please refer to [Multilingual OCR Development Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
+
 
 <a name="LICENSE"></a>
 ## License
@@ -149,3 +175,5 @@ We welcome all the contributions to PaddleOCR and appreciate for your feedback v
 - Thanks [authorfu](https://github.com/authorfu) for contributing Android demo  and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
 - Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
 - Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
+- Thanks [lijinhan](https://github.com/lijinhan) for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
+- Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set.

README_ch.md

@@ -4,11 +4,11 @@
 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。
 
 **近期更新**
+- 2020.10.26 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，共计94个常见问题及解答，并且计划以后每周一都会更新，欢迎大家持续关注。
 - 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
-- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
+- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipeline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
 - 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。[模型下载](#模型下载)
 - 2020.9.17 更新[英文识别模型](./doc/doc_ch/models_list.md#英文识别模型)和[多语言识别模型](doc/doc_ch/models_list.md#多语言识别模型)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
-- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
 - 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
 - 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
 - [More](./doc/doc_ch/update.md)
@@ -35,6 +35,15 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
 
+<a name="欢迎加入PaddleOCR技术交流群"></a>
+## 欢迎加入PaddleOCR技术交流群
+- 微信扫描二维码加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
+
+<div align="center">
+<img src="./doc/joinus.PNG"  width = "200" height = "200" />
+</div>
+
+
 ## 快速体验
 - PC端：超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
 
@@ -65,7 +74,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - 算法介绍
     - [文本检测](./doc/doc_ch/algorithm_overview.md)
     - [文本识别](./doc/doc_ch/algorithm_overview.md)
-    - [PP-OCR Pipline](#PP-OCR)
+    - [PP-OCR Pipeline](#PP-OCR)
 - 模型训练/评估
     - [文本检测](./doc/doc_ch/detection.md)
     - [文本识别](./doc/doc_ch/recognition.md)
@@ -75,7 +84,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
     - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
     - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
     - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
-    - [服务化部署](./deploy/hubserving/readme.md)
+    - [服务化部署](./doc/doc_ch/serving_inference.md)
     - [端侧部署](./deploy/lite/readme.md)
     - [模型量化](./deploy/slim/quantization/README.md)
     - [模型裁剪](./deploy/slim/prune/README.md)
@@ -89,15 +98,15 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - [效果展示](#效果展示)
 - FAQ
     - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
-    - [【理论篇】OCR通用21个问题](./doc/doc_ch/FAQ.md)
-    - [【实战篇】PaddleOCR实战53个问题](./doc/doc_ch/FAQ.md)
+    - [【理论篇】OCR通用23个问题](./doc/doc_ch/FAQ.md)
+    - [【实战篇】PaddleOCR实战61个问题](./doc/doc_ch/FAQ.md)
 - [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="PP-OCR"></a>
-## PP-OCR Pipline
+## PP-OCR Pipeline
 <div align="center">
     <img src="./doc/ppocr_framework.png" width="800">
 </div>
@@ -125,13 +134,7 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框
     <img src="./doc/imgs_results/1112.jpg" width="800">
 </div>
 
-<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.PNG"  width = "200" height = "200" />
-</div>
 
 <a name="许可证书"></a>
 ## 许可证书
@@ -148,3 +151,5 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框
 - 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
 - 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
 - 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
+- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用。
+- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料。

configs/det/det_db_icdar15_reader.yml

@@ -18,5 +18,4 @@ TestReader:
   infer_img:
   img_set_dir: ./train_data/icdar2015/text_localization/
   label_file_path: ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
-  test_image_shape: [736, 1280]
   do_eval: True

configs/det/det_r18_vd_db_v1.1.yml

@@ -51,7 +51,7 @@ Optimizer:
 PostProcess:
   function: ppocr.postprocess.db_postprocess,DBPostProcess
   thresh: 0.3
-  box_thresh: 0.6
+  box_thresh: 0.5
   max_candidates: 1000
-  unclip_ratio: 1.5
+  unclip_ratio: 1.6
   

configs/rec/multi_languages/rec_french_lite_train.yml

@@ -12,7 +12,7 @@ Global:
   image_shape: [3, 32, 320]
   max_text_length: 25
   character_type: french
-  character_dict_path: ./ppocr/utils/french_dict.txt
+  character_dict_path: ./ppocr/utils/dict/french_dict.txt
   loss_type: ctc
   distort: true
   use_space_char: false

configs/rec/multi_languages/rec_ger_lite_train.yml

@@ -12,7 +12,7 @@ Global:
   image_shape: [3, 32, 320]
   max_text_length: 25
   character_type: german
-  character_dict_path: ./ppocr/utils/german_dict.txt
+  character_dict_path: ./ppocr/utils/dict/german_dict.txt
   loss_type: ctc
   distort: true
   use_space_char: false

configs/rec/multi_languages/rec_japan_lite_train.yml

@@ -12,7 +12,7 @@ Global:
   image_shape: [3, 32, 320]
   max_text_length: 25
   character_type: japan
-  character_dict_path: ./ppocr/utils/japan_dict.txt
+  character_dict_path: ./ppocr/utils/dict/japan_dict.txt
   loss_type: ctc
   distort: true
   use_space_char: false

configs/rec/multi_languages/rec_korean_lite_train.yml

@@ -12,7 +12,7 @@ Global:
   image_shape: [3, 32, 320]
   max_text_length: 25
   character_type: korean
-  character_dict_path: ./ppocr/utils/korean_dict.txt
+  character_dict_path: ./ppocr/utils/dict/korean_dict.txt
   loss_type: ctc
   distort: true
   use_space_char: false

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:

configs/rec/rec_mv3_none_none_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights: 

configs/rec/rec_mv3_tps_bilstm_attn.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: attention
   tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:

configs/rec/rec_r34_vd_tps_bilstm_attn.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: attention
   tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: ctc
   tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml

configs/rec/rec_r50fpn_vd_none_srn.yml

@@ -12,6 +12,7 @@ Global:
   image_shape: [1, 64, 256]
   max_text_length: 25
   character_type: en
+  character_dict_path:
   loss_type: srn
   num_heads: 8
   average_window: 0.15

deploy/android_demo/README.md

@@ -2,7 +2,7 @@
 ### 1. 安装最新版本的Android Studio
 可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
 
-### 2. 按照NDK 20 以上版本 
+### 2. 按照NDK 20 以上版本
 Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
 
 如果您是初学者，可以用以下方式安装和测试NDK编译环境。
@@ -17,3 +17,10 @@ Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编
 
 - Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
 - SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发
+
+
+# FAQ：
+Q1: 更新1.1版本的模型后，demo报错？
+
+
+A1. 如果要更换V1.1 版本的模型，请更新模型的同时，更新预测库文件，建议使用[PaddleLite 2.6.3](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3)版本的预测库文件，OCR移动端部署参考[教程](../lite/readme.md)。

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

@@ -35,12 +35,6 @@ public class OCRPredictorNative {
 
     }
 
-    public void release() {
-        if (nativePointer != 0) {
-            nativePointer = 0;
-//            destory(nativePointer);
-        }
-    }
 
     public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
         Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
@@ -63,7 +57,7 @@ public class OCRPredictorNative {
 
     protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
 
-    protected native void destory(long pointer);
+    public native void release(long pointer);
 
     private ArrayList<OcrResultModel> postprocess(float[] raw) {
         ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();

deploy/cpp_infer/include/preprocess_op.h

@@ -59,7 +59,7 @@ public:
 class ClsResizeImg {
 public:
   virtual void Run(const cv::Mat &img, cv::Mat &resize_img,
-                   const std::vector<int> &rec_image_shape = {3, 32, 320});
+                   const std::vector<int> &rec_image_shape = {3, 48, 192});
 };
 
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/src/ocr_cls.cpp

@@ -21,7 +21,7 @@ cv::Mat Classifier::Run(cv::Mat &img) {
   img.copyTo(src_img);
   cv::Mat resize_img;
 
-  std::vector<int> rec_image_shape = {3, 32, 100};
+  std::vector<int> rec_image_shape = {3, 48, 192};
   int index = 0;
   float wh_ratio = float(img.cols) / float(img.rows);
 

deploy/cpp_infer/src/preprocess_op.cpp

@@ -85,7 +85,7 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
 
   if (resize_w % 32 == 0)
     resize_w = resize_w;
-  else if (resize_w / 32 < 1)
+  else if (resize_w / 32 < 1 + 1e-5)
     resize_w = 32;
   else
     resize_w = (resize_w / 32 - 1) * 32;
@@ -138,4 +138,4 @@ void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
   }
 }
 
-} // namespace PaddleOCR
+} // namespace PaddleOCR
\ No newline at end of file

deploy/hubserving/ocr_det/module.py

@@ -3,11 +3,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+import sys
+sys.path.insert(0, ".")
+
 import argparse
 import ast
 import copy
 import math
-import os
 import time
 
 from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
@@ -67,9 +70,7 @@ class OCRDet(hub.Module):
             images.append(img)
         return images
 
-    def predict(self,
-                images=[],
-                paths=[]):
+    def predict(self, images=[], paths=[]):
         """
         Get the text box in the predicted images.
         Args:
@@ -87,7 +88,7 @@ class OCRDet(hub.Module):
             raise TypeError("The input data is inconsistent with expectations.")
 
         assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
-        
+
         all_results = []
         for img in predicted_data:
             if img is None:
@@ -99,11 +100,9 @@ class OCRDet(hub.Module):
 
             rec_res_final = []
             for dno in range(len(dt_boxes)):
-                rec_res_final.append(
-                    {
-                        'text_region': dt_boxes[dno].astype(np.int).tolist()
-                    }
-                )
+                rec_res_final.append({
+                    'text_region': dt_boxes[dno].astype(np.int).tolist()
+                })
             all_results.append(rec_res_final)
         return all_results
 
@@ -116,7 +115,7 @@ class OCRDet(hub.Module):
         results = self.predict(images_decode, **kwargs)
         return results
 
-   
+
 if __name__ == '__main__':
     ocr = OCRDet()
     image_path = [
@@ -124,4 +123,4 @@ if __name__ == '__main__':
         './doc/imgs/12.jpg',
     ]
     res = ocr.predict(paths=image_path)
-    print(res)
+    print(res)
\ No newline at end of file

deploy/hubserving/ocr_rec/module.py

@@ -3,11 +3,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+import sys
+sys.path.insert(0, ".")
+
 import argparse
 import ast
 import copy
 import math
-import os
 import time
 
 from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
@@ -67,9 +70,7 @@ class OCRRec(hub.Module):
             images.append(img)
         return images
 
-    def predict(self,
-                images=[],
-                paths=[]):
+    def predict(self, images=[], paths=[]):
         """
         Get the text box in the predicted images.
         Args:
@@ -87,31 +88,28 @@ class OCRRec(hub.Module):
             raise TypeError("The input data is inconsistent with expectations.")
 
         assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
-        
+
         img_list = []
         for img in predicted_data:
             if img is None:
                 continue
             img_list.append(img)
-            
+
         rec_res_final = []
         try:
             rec_res, predict_time = self.text_recognizer(img_list)
             for dno in range(len(rec_res)):
                 text, score = rec_res[dno]
-                rec_res_final.append(
-                    {
-                        'text': text,
-                        'confidence': float(score),
-                    }
-                )
+                rec_res_final.append({
+                    'text': text,
+                    'confidence': float(score),
+                })
         except Exception as e:
             print(e)
             return [[]]
 
         return [rec_res_final]
 
-
     @serving
     def serving_method(self, images, **kwargs):
         """
@@ -121,7 +119,7 @@ class OCRRec(hub.Module):
         results = self.predict(images_decode, **kwargs)
         return results
 
-   
+
 if __name__ == '__main__':
     ocr = OCRRec()
     image_path = [
@@ -130,4 +128,4 @@ if __name__ == '__main__':
         './doc/imgs_words/ch/word_3.jpg',
     ]
     res = ocr.predict(paths=image_path)
-    print(res)
+    print(res)
\ No newline at end of file

deploy/hubserving/ocr_system/module.py

@@ -3,11 +3,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+import sys
+sys.path.insert(0, ".")
+
 import argparse
 import ast
 import copy
 import math
-import os
 import time
 
 from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
@@ -52,7 +55,7 @@ class OCRSystem(hub.Module):
                 )
         cfg.ir_optim = True
         cfg.enable_mkldnn = enable_mkldnn
-        
+
         self.text_sys = TextSystem(cfg)
 
     def read_images(self, paths=[]):
@@ -67,9 +70,7 @@ class OCRSystem(hub.Module):
             images.append(img)
         return images
 
-    def predict(self,
-                       images=[],
-                       paths=[]):
+    def predict(self, images=[], paths=[]):
         """
         Get the chinese texts in the predicted images.
         Args:
@@ -104,13 +105,11 @@ class OCRSystem(hub.Module):
 
             for dno in range(dt_num):
                 text, score = rec_res[dno]
-                rec_res_final.append(
-                    {
-                        'text': text,
-                        'confidence': float(score),
-                        'text_region': dt_boxes[dno].astype(np.int).tolist()
-                    }
-                )
+                rec_res_final.append({
+                    'text': text,
+                    'confidence': float(score),
+                    'text_region': dt_boxes[dno].astype(np.int).tolist()
+                })
             all_results.append(rec_res_final)
         return all_results
 
@@ -123,7 +122,7 @@ class OCRSystem(hub.Module):
         results = self.predict(images_decode, **kwargs)
         return results
 
-   
+
 if __name__ == '__main__':
     ocr = OCRSystem()
     image_path = [
@@ -131,4 +130,4 @@ if __name__ == '__main__':
         './doc/imgs/12.jpg',
     ]
     res = ocr.predict(paths=image_path)
-    print(res)
+    print(res)
\ No newline at end of file

deploy/hubserving/readme.md

 [English](readme_en.md) | 简体中文
 
 PaddleOCR提供2种服务部署方式：
-- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；	
-- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../pdserving/readme.md)。
+- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；
+- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../../doc/doc_ch/serving_inference.md)。
 
 # 基于PaddleHub Serving的服务部署
 
@@ -29,12 +29,6 @@ deploy/hubserving/ocr_system/
 ```shell
 # 安装paddlehub  
 pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-# 在Linux下设置环境变量
-export PYTHONPATH=.
-
-# 或者，在Windows下设置环境变量
-SET PYTHONPATH=.
 ```
 
 ### 2. 下载推理模型

deploy/hubserving/readme_en.md

 English | [简体中文](readme.md)
 
-PaddleOCR provides 2 service deployment methods: 
-- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial. 
-- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../pdserving/readme_en.md) for usage.
+PaddleOCR provides 2 service deployment methods:
+- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
+- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../doc/doc_ch/serving_inference.md) for usage.
 
 # Service deployment based on PaddleHub Serving  
 
@@ -30,12 +30,6 @@ The following steps take the 2-stage series service as an example. If only the d
 ```shell
 # Install paddlehub  
 pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-# Set environment variables on Linux
-export PYTHONPATH=.
-
-# Set environment variables on Windows
-SET PYTHONPATH=.
 ```
 
 ### 2. Download inference model

deploy/lite/readme.md

@@ -25,17 +25,16 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 - 1. 直接下载，预测库下载链接如下：
       |平台|预测库下载链接|
       |-|-|
-      |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
-      |IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
 
-      注：1. 如果是从下Paddle-Lite[官网文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc)下载的预测库，
-      注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 如果使用量化的模型部署在端侧，建议使用Paddle-Lite develop分支编译预测库。
+      注：1. 上述预测库为PaddleLite 2.6.3分支编译得到，有关PaddleLite 2.6.3 详细信息可参考[链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3)。
 
-- 2. [建议]编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
+- 2. [推荐]编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# 务必使用develop分支编译预测库
+# 切换到Paddle-Lite develop稳定分支
 git checkout develop
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
@@ -101,6 +100,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
 git checkout develop
+# 切换到固定的commit
+git reset --hard 55c53482bcdd2868373d024dd1144e4c5ec0e6b8
 # 启动编译
 ./lite/tools/build.sh build_optimize_tool
 ```
@@ -221,11 +222,11 @@ demo/cxx/ocr/
 1. ppocr_keys_v1.txt是中文字典文件，如果使用的 nb 模型是英文数字或其他语言的模型，需要更换为对应语言的字典。
 PaddleOCR 在ppocr/utils/下存放了多种字典，包括：
 ```
-french_dict.txt     # 法语字典
-german_dict.txt     # 德语字典
+dict/french_dict.txt     # 法语字典
+dict/german_dict.txt     # 德语字典
 ic15_dict.txt       # 英文字典
-japan_dict.txt      # 日语字典
-korean_dict.txt     # 韩语字典
+dict/japan_dict.txt      # 日语字典
+dict/korean_dict.txt     # 韩语字典
 ppocr_keys_v1.txt   # 中文字典
 ```
 
@@ -235,7 +236,7 @@ max_side_len  960         # 输入图像长宽大于960时，等比例缩放图
 det_db_thresh  0.3        # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
 det_db_box_thresh  0.5    # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
 det_db_unclip_ratio  1.6  # 表示文本框的紧致程度，越小则文本框更靠近文本
-use_direction_classify  1  # 是否使用方向分类器，0表示不使用，1表示使用
+use_direction_classify  0  # 是否使用方向分类器，0表示不使用，1表示使用
 ```
 
  5. 启动调试
@@ -253,7 +254,7 @@ use_direction_classify  1  # 是否使用方向分类器，0表示不使用，1
  adb push debug /data/local/tmp/
  adb shell
  cd /data/local/tmp/debug
- export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
+ export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
  ./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb  ch_ppocr_mobile_v1.1_rec_quant_opt.nb  ch_ppocr_mobile_cls_quant_opt.nb  ./11.jpg  ppocr_keys_v1.txt
  ```
 

deploy/lite/readme_en.md

 
 # Tutorial of PaddleOCR Mobile deployment
 
-This tutorial will introduce how to use paddle-lite to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
+This tutorial will introduce how to use [paddle-lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
 
 paddle-lite is a lightweight inference engine for PaddlePaddle.
-It provides efficient inference capabilities for mobile phones and IOTs,
+It provides efficient inference capabilities for mobile phones and IoTs,
 and extensively integrates cross-platform hardware to provide lightweight
 deployment solutions for end-side deployment issues.
 
@@ -22,10 +22,10 @@ deployment solutions for end-side deployment issues.
 
 |Platform|Prebuild library Download Link|
 |-|-|
-|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
-|IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
+|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+|IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
 
-note: It is recommended to build prebuild library using [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) develop branch if developer wants to deploy the [quantitative](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md) model to mobile phone.
+note: The above pre-build inference library is compiled from the PaddleLite `release/2.6.3` branch. For more information about PaddleLite 2.6.3, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3).
 
 
 The structure of the prediction library is as follows:
@@ -65,11 +65,11 @@ If you have prepared the model file ending in `.nb`, you can skip this step.
 The following table also provides a series of models that can be deployed on mobile phones to recognize Chinese.
 You can directly download the optimized model.
 
-|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle Lite branch |
-|-|-|-|-|-|-|
-|V1.1|extra-lightweight chinese OCR optimized model|8.1M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_opt.nb)|develop|
-|[slim] V1.1|extra-lightweight chinese OCR optimized model|3.5M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop|
-|V1.0|lightweight Chinese OCR optimized model|8.6M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_det_opt.nb)|---|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_rec_opt.nb)|develop|
+| Version | Introduction | Model size | Detection model | Text Direction model | Recognition model | Paddle Lite branch |
+| - | - | - | - | - | - | - |
+| V1.1 | extra-lightweight chinese OCR optimized model | 8.1M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_opt.nb) | develop |
+| [slim] V1.1 | extra-lightweight chinese OCR optimized model | 3.5M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) | develop |
+| V1.0 | lightweight Chinese OCR optimized model | 8.6M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_det_opt.nb) | - | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_rec_opt.nb) | develop |
 
 If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model.
 
@@ -77,6 +77,8 @@ If the model to be deployed is not in the above table, you need to follow the st
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
 git checkout develop
+# switch to the specified commit
+git reset --hard 55c53482bcdd2868373d024dd1144e4c5ec0e6b8
 ./lite/tools/build.sh build_optimize_tool
 ```
 
@@ -185,11 +187,11 @@ demo/cxx/ocr/
 If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language.
 PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
 ```
-french_dict.txt     # french
-german_dict.txt     # german
+dict/french_dict.txt     # french
+dict/german_dict.txt     # german
 ic15_dict.txt       # english
-japan_dict.txt      # japan
-korean_dict.txt     # korean
+dict/japan_dict.txt      # japan
+dict/korean_dict.txt     # korean
 ppocr_keys_v1.txt   # chinese
 ```
 
@@ -199,7 +201,7 @@ max_side_len  960         #  Limit the maximum image height and width to 960
 det_db_thresh  0.3        # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
 det_db_box_thresh  0.5    # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
 det_db_unclip_ratio  1.6  # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
-use_direction_classify  1  # Whether to use the direction classifier, 0 means not to use, 1 means to use
+use_direction_classify  0  # Whether to use the direction classifier, 0 means not to use, 1 means to use
 ```
 
 5. Run Model on phone

deploy/paddleOcrSpringBoot/mvnw

+#!/bin/sh
+# ----------------------------------------------------------------------------
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------------
+# Maven Start Up Batch script
+#
+# Required ENV vars:
+# ------------------
+#   JAVA_HOME - location of a JDK home dir
+#
+# Optional ENV vars
+# -----------------
+#   M2_HOME - location of maven2's installed home dir
+#   MAVEN_OPTS - parameters passed to the Java VM when running Maven
+#     e.g. to debug Maven itself, use
+#       set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
+#   MAVEN_SKIP_RC - flag to disable loading of mavenrc files
+# ----------------------------------------------------------------------------
+
+if [ -z "$MAVEN_SKIP_RC" ] ; then
+
+  if [ -f /etc/mavenrc ] ; then
+    . /etc/mavenrc
+  fi
+
+  if [ -f "$HOME/.mavenrc" ] ; then
+    . "$HOME/.mavenrc"
+  fi
+
+fi
+
+# OS specific support.  $var _must_ be set to either true or false.
+cygwin=false;
+darwin=false;
+mingw=false
+case "`uname`" in
+  CYGWIN*) cygwin=true ;;
+  MINGW*) mingw=true;;
+  Darwin*) darwin=true
+    # Use /usr/libexec/java_home if available, otherwise fall back to /Library/Java/Home
+    # See https://developer.apple.com/library/mac/qa/qa1170/_index.html
+    if [ -z "$JAVA_HOME" ]; then
+      if [ -x "/usr/libexec/java_home" ]; then
+        export JAVA_HOME="`/usr/libexec/java_home`"
+      else
+        export JAVA_HOME="/Library/Java/Home"
+      fi
+    fi
+    ;;
+esac
+
+if [ -z "$JAVA_HOME" ] ; then
+  if [ -r /etc/gentoo-release ] ; then
+    JAVA_HOME=`java-config --jre-home`
+  fi
+fi
+
+if [ -z "$M2_HOME" ] ; then
+  ## resolve links - $0 may be a link to maven's home
+  PRG="$0"
+
+  # need this for relative symlinks
+  while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+      PRG="$link"
+    else
+      PRG="`dirname "$PRG"`/$link"
+    fi
+  done
+
+  saveddir=`pwd`
+
+  M2_HOME=`dirname "$PRG"`/..
+
+  # make it fully qualified
+  M2_HOME=`cd "$M2_HOME" && pwd`
+
+  cd "$saveddir"
+  # echo Using m2 at $M2_HOME
+fi
+
+# For Cygwin, ensure paths are in UNIX format before anything is touched
+if $cygwin ; then
+  [ -n "$M2_HOME" ] &&
+    M2_HOME=`cygpath --unix "$M2_HOME"`
+  [ -n "$JAVA_HOME" ] &&
+    JAVA_HOME=`cygpath --unix "$JAVA_HOME"`
+  [ -n "$CLASSPATH" ] &&
+    CLASSPATH=`cygpath --path --unix "$CLASSPATH"`
+fi
+
+# For Mingw, ensure paths are in UNIX format before anything is touched
+if $mingw ; then
+  [ -n "$M2_HOME" ] &&
+    M2_HOME="`(cd "$M2_HOME"; pwd)`"
+  [ -n "$JAVA_HOME" ] &&
+    JAVA_HOME="`(cd "$JAVA_HOME"; pwd)`"
+fi
+
+if [ -z "$JAVA_HOME" ]; then
+  javaExecutable="`which javac`"
+  if [ -n "$javaExecutable" ] && ! [ "`expr \"$javaExecutable\" : '\([^ ]*\)'`" = "no" ]; then
+    # readlink(1) is not available as standard on Solaris 10.
+    readLink=`which readlink`
+    if [ ! `expr "$readLink" : '\([^ ]*\)'` = "no" ]; then
+      if $darwin ; then
+        javaHome="`dirname \"$javaExecutable\"`"
+        javaExecutable="`cd \"$javaHome\" && pwd -P`/javac"
+      else
+        javaExecutable="`readlink -f \"$javaExecutable\"`"
+      fi
+      javaHome="`dirname \"$javaExecutable\"`"
+      javaHome=`expr "$javaHome" : '\(.*\)/bin'`
+      JAVA_HOME="$javaHome"
+      export JAVA_HOME
+    fi
+  fi
+fi
+
+if [ -z "$JAVACMD" ] ; then
+  if [ -n "$JAVA_HOME"  ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+      # IBM's JDK on AIX uses strange locations for the executables
+      JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+      JAVACMD="$JAVA_HOME/bin/java"
+    fi
+  else
+    JAVACMD="`which java`"
+  fi
+fi
+
+if [ ! -x "$JAVACMD" ] ; then
+  echo "Error: JAVA_HOME is not defined correctly." >&2
+  echo "  We cannot execute $JAVACMD" >&2
+  exit 1
+fi
+
+if [ -z "$JAVA_HOME" ] ; then
+  echo "Warning: JAVA_HOME environment variable is not set."
+fi
+
+CLASSWORLDS_LAUNCHER=org.codehaus.plexus.classworlds.launcher.Launcher
+
+# traverses directory structure from process work directory to filesystem root
+# first directory with .mvn subdirectory is considered project base directory
+find_maven_basedir() {
+
+  if [ -z "$1" ]
+  then
+    echo "Path not specified to find_maven_basedir"
+    return 1
+  fi
+
+  basedir="$1"
+  wdir="$1"
+  while [ "$wdir" != '/' ] ; do
+    if [ -d "$wdir"/.mvn ] ; then
+      basedir=$wdir
+      break
+    fi
+    # workaround for JBEAP-8937 (on Solaris 10/Sparc)
+    if [ -d "${wdir}" ]; then
+      wdir=`cd "$wdir/.."; pwd`
+    fi
+    # end of workaround
+  done
+  echo "${basedir}"
+}
+
+# concatenates all lines of a file
+concat_lines() {
+  if [ -f "$1" ]; then
+    echo "$(tr -s '\n' ' ' < "$1")"
+  fi
+}
+
+BASE_DIR=`find_maven_basedir "$(pwd)"`
+if [ -z "$BASE_DIR" ]; then
+  exit 1;
+fi
+
+##########################################################################################
+# Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
+# This allows using the maven wrapper in projects that prohibit checking in binary data.
+##########################################################################################
+if [ -r "$BASE_DIR/.mvn/wrapper/maven-wrapper.jar" ]; then
+    if [ "$MVNW_VERBOSE" = true ]; then
+      echo "Found .mvn/wrapper/maven-wrapper.jar"
+    fi
+else
+    if [ "$MVNW_VERBOSE" = true ]; then
+      echo "Couldn't find .mvn/wrapper/maven-wrapper.jar, downloading it ..."
+    fi
+    if [ -n "$MVNW_REPOURL" ]; then
+      jarUrl="$MVNW_REPOURL/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
+    else
+      jarUrl="https://repo.maven.apache.org/maven2/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
+    fi
+    while IFS="=" read key value; do
+      case "$key" in (wrapperUrl) jarUrl="$value"; break ;;
+      esac
+    done < "$BASE_DIR/.mvn/wrapper/maven-wrapper.properties"
+    if [ "$MVNW_VERBOSE" = true ]; then
+      echo "Downloading from: $jarUrl"
+    fi
+    wrapperJarPath="$BASE_DIR/.mvn/wrapper/maven-wrapper.jar"
+    if $cygwin; then
+      wrapperJarPath=`cygpath --path --windows "$wrapperJarPath"`
+    fi
+
+    if command -v wget > /dev/null; then
+        if [ "$MVNW_VERBOSE" = true ]; then
+          echo "Found wget ... using wget"
+        fi
+        if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
+            wget "$jarUrl" -O "$wrapperJarPath"
+        else
+            wget --http-user=$MVNW_USERNAME --http-password=$MVNW_PASSWORD "$jarUrl" -O "$wrapperJarPath"
+        fi
+    elif command -v curl > /dev/null; then
+        if [ "$MVNW_VERBOSE" = true ]; then
+          echo "Found curl ... using curl"
+        fi
+        if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
+            curl -o "$wrapperJarPath" "$jarUrl" -f
+        else
+            curl --user $MVNW_USERNAME:$MVNW_PASSWORD -o "$wrapperJarPath" "$jarUrl" -f
+        fi
+
+    else
+        if [ "$MVNW_VERBOSE" = true ]; then
+          echo "Falling back to using Java to download"
+        fi
+        javaClass="$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.java"
+        # For Cygwin, switch paths to Windows format before running javac
+        if $cygwin; then
+          javaClass=`cygpath --path --windows "$javaClass"`
+        fi
+        if [ -e "$javaClass" ]; then
+            if [ ! -e "$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.class" ]; then
+                if [ "$MVNW_VERBOSE" = true ]; then
+                  echo " - Compiling MavenWrapperDownloader.java ..."
+                fi
+                # Compiling the Java class
+                ("$JAVA_HOME/bin/javac" "$javaClass")
+            fi
+            if [ -e "$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.class" ]; then
+                # Running the downloader
+                if [ "$MVNW_VERBOSE" = true ]; then
+                  echo " - Running MavenWrapperDownloader.java ..."
+                fi
+                ("$JAVA_HOME/bin/java" -cp .mvn/wrapper MavenWrapperDownloader "$MAVEN_PROJECTBASEDIR")
+            fi
+        fi
+    fi
+fi
+##########################################################################################
+# End of extension
+##########################################################################################
+
+export MAVEN_PROJECTBASEDIR=${MAVEN_BASEDIR:-"$BASE_DIR"}
+if [ "$MVNW_VERBOSE" = true ]; then
+  echo $MAVEN_PROJECTBASEDIR
+fi
+MAVEN_OPTS="$(concat_lines "$MAVEN_PROJECTBASEDIR/.mvn/jvm.config") $MAVEN_OPTS"
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin; then
+  [ -n "$M2_HOME" ] &&
+    M2_HOME=`cygpath --path --windows "$M2_HOME"`
+  [ -n "$JAVA_HOME" ] &&
+    JAVA_HOME=`cygpath --path --windows "$JAVA_HOME"`
+  [ -n "$CLASSPATH" ] &&
+    CLASSPATH=`cygpath --path --windows "$CLASSPATH"`
+  [ -n "$MAVEN_PROJECTBASEDIR" ] &&
+    MAVEN_PROJECTBASEDIR=`cygpath --path --windows "$MAVEN_PROJECTBASEDIR"`
+fi
+
+# Provide a "standardized" way to retrieve the CLI args that will
+# work with both Windows and non-Windows executions.
+MAVEN_CMD_LINE_ARGS="$MAVEN_CONFIG $@"
+export MAVEN_CMD_LINE_ARGS
+
+WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
+
+exec "$JAVACMD" \
+  $MAVEN_OPTS \
+  -classpath "$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.jar" \
+  "-Dmaven.home=${M2_HOME}" "-Dmaven.multiModuleProjectDirectory=${MAVEN_PROJECTBASEDIR}" \
+  ${WRAPPER_LAUNCHER} $MAVEN_CONFIG "$@"

deploy/paddleOcrSpringBoot/mvnw.cmd

+@REM ----------------------------------------------------------------------------
+@REM Licensed to the Apache Software Foundation (ASF) under one
+@REM or more contributor license agreements.  See the NOTICE file
+@REM distributed with this work for additional information
+@REM regarding copyright ownership.  The ASF licenses this file
+@REM to you under the Apache License, Version 2.0 (the
+@REM "License"); you may not use this file except in compliance
+@REM with the License.  You may obtain a copy of the License at
+@REM
+@REM    https://www.apache.org/licenses/LICENSE-2.0
+@REM
+@REM Unless required by applicable law or agreed to in writing,
+@REM software distributed under the License is distributed on an
+@REM "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+@REM KIND, either express or implied.  See the License for the
+@REM specific language governing permissions and limitations
+@REM under the License.
+@REM ----------------------------------------------------------------------------
+
+@REM ----------------------------------------------------------------------------
+@REM Maven Start Up Batch script
+@REM
+@REM Required ENV vars:
+@REM JAVA_HOME - location of a JDK home dir
+@REM
+@REM Optional ENV vars
+@REM M2_HOME - location of maven2's installed home dir
+@REM MAVEN_BATCH_ECHO - set to 'on' to enable the echoing of the batch commands
+@REM MAVEN_BATCH_PAUSE - set to 'on' to wait for a keystroke before ending
+@REM MAVEN_OPTS - parameters passed to the Java VM when running Maven
+@REM     e.g. to debug Maven itself, use
+@REM set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
+@REM MAVEN_SKIP_RC - flag to disable loading of mavenrc files
+@REM ----------------------------------------------------------------------------
+
+@REM Begin all REM lines with '@' in case MAVEN_BATCH_ECHO is 'on'
+@echo off
+@REM set title of command window
+title %0
+@REM enable echoing by setting MAVEN_BATCH_ECHO to 'on'
+@if "%MAVEN_BATCH_ECHO%" == "on"  echo %MAVEN_BATCH_ECHO%
+
+@REM set %HOME% to equivalent of $HOME
+if "%HOME%" == "" (set "HOME=%HOMEDRIVE%%HOMEPATH%")
+
+@REM Execute a user defined script before this one
+if not "%MAVEN_SKIP_RC%" == "" goto skipRcPre
+@REM check for pre script, once with legacy .bat ending and once with .cmd ending
+if exist "%HOME%\mavenrc_pre.bat" call "%HOME%\mavenrc_pre.bat"
+if exist "%HOME%\mavenrc_pre.cmd" call "%HOME%\mavenrc_pre.cmd"
+:skipRcPre
+
+@setlocal
+
+set ERROR_CODE=0
+
+@REM To isolate internal variables from possible post scripts, we use another setlocal
+@setlocal
+
+@REM ==== START VALIDATION ====
+if not "%JAVA_HOME%" == "" goto OkJHome
+
+echo.
+echo Error: JAVA_HOME not found in your environment. >&2
+echo Please set the JAVA_HOME variable in your environment to match the >&2
+echo location of your Java installation. >&2
+echo.
+goto error
+
+:OkJHome
+if exist "%JAVA_HOME%\bin\java.exe" goto init
+
+echo.
+echo Error: JAVA_HOME is set to an invalid directory. >&2
+echo JAVA_HOME = "%JAVA_HOME%" >&2
+echo Please set the JAVA_HOME variable in your environment to match the >&2
+echo location of your Java installation. >&2
+echo.
+goto error
+
+@REM ==== END VALIDATION ====
+
+:init
+
+@REM Find the project base dir, i.e. the directory that contains the folder ".mvn".
+@REM Fallback to current working directory if not found.
+
+set MAVEN_PROJECTBASEDIR=%MAVEN_BASEDIR%
+IF NOT "%MAVEN_PROJECTBASEDIR%"=="" goto endDetectBaseDir
+
+set EXEC_DIR=%CD%
+set WDIR=%EXEC_DIR%
+:findBaseDir
+IF EXIST "%WDIR%"\.mvn goto baseDirFound
+cd ..
+IF "%WDIR%"=="%CD%" goto baseDirNotFound
+set WDIR=%CD%
+goto findBaseDir
+
+:baseDirFound
+set MAVEN_PROJECTBASEDIR=%WDIR%
+cd "%EXEC_DIR%"
+goto endDetectBaseDir
+
+:baseDirNotFound
+set MAVEN_PROJECTBASEDIR=%EXEC_DIR%
+cd "%EXEC_DIR%"
+
+:endDetectBaseDir
+
+IF NOT EXIST "%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config" goto endReadAdditionalConfig
+
+@setlocal EnableExtensions EnableDelayedExpansion
+for /F "usebackq delims=" %%a in ("%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config") do set JVM_CONFIG_MAVEN_PROPS=!JVM_CONFIG_MAVEN_PROPS! %%a
+@endlocal & set JVM_CONFIG_MAVEN_PROPS=%JVM_CONFIG_MAVEN_PROPS%
+
+:endReadAdditionalConfig
+
+SET MAVEN_JAVA_EXE="%JAVA_HOME%\bin\java.exe"
+set WRAPPER_JAR="%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.jar"
+set WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
+
+set DOWNLOAD_URL="https://repo.maven.apache.org/maven2/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
+
+FOR /F "tokens=1,2 delims==" %%A IN ("%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.properties") DO (
+    IF "%%A"=="wrapperUrl" SET DOWNLOAD_URL=%%B
+)
+
+@REM Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
+@REM This allows using the maven wrapper in projects that prohibit checking in binary data.
+if exist %WRAPPER_JAR% (
+    if "%MVNW_VERBOSE%" == "true" (
+        echo Found %WRAPPER_JAR%
+    )
+) else (
+    if not "%MVNW_REPOURL%" == "" (
+        SET DOWNLOAD_URL="%MVNW_REPOURL%/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
+    )
+    if "%MVNW_VERBOSE%" == "true" (
+        echo Couldn't find %WRAPPER_JAR%, downloading it ...
+        echo Downloading from: %DOWNLOAD_URL%
+    )
+
+    powershell -Command "&{"^
+		"$webclient = new-object System.Net.WebClient;"^
+		"if (-not ([string]::IsNullOrEmpty('%MVNW_USERNAME%') -and [string]::IsNullOrEmpty('%MVNW_PASSWORD%'))) {"^
+		"$webclient.Credentials = new-object System.Net.NetworkCredential('%MVNW_USERNAME%', '%MVNW_PASSWORD%');"^
+		"}"^
+		"[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; $webclient.DownloadFile('%DOWNLOAD_URL%', '%WRAPPER_JAR%')"^
+		"}"
+    if "%MVNW_VERBOSE%" == "true" (
+        echo Finished downloading %WRAPPER_JAR%
+    )
+)
+@REM End of extension
+
+@REM Provide a "standardized" way to retrieve the CLI args that will
+@REM work with both Windows and non-Windows executions.
+set MAVEN_CMD_LINE_ARGS=%*
+
+%MAVEN_JAVA_EXE% %JVM_CONFIG_MAVEN_PROPS% %MAVEN_OPTS% %MAVEN_DEBUG_OPTS% -classpath %WRAPPER_JAR% "-Dmaven.multiModuleProjectDirectory=%MAVEN_PROJECTBASEDIR%" %WRAPPER_LAUNCHER% %MAVEN_CONFIG% %*
+if ERRORLEVEL 1 goto error
+goto end
+
+:error
+set ERROR_CODE=1
+
+:end
+@endlocal & set ERROR_CODE=%ERROR_CODE%
+
+if not "%MAVEN_SKIP_RC%" == "" goto skipRcPost
+@REM check for post script, once with legacy .bat ending and once with .cmd ending
+if exist "%HOME%\mavenrc_post.bat" call "%HOME%\mavenrc_post.bat"
+if exist "%HOME%\mavenrc_post.cmd" call "%HOME%\mavenrc_post.cmd"
+:skipRcPost
+
+@REM pause the script if MAVEN_BATCH_PAUSE is set to 'on'
+if "%MAVEN_BATCH_PAUSE%" == "on" pause
+
+if "%MAVEN_TERMINATE_CMD%" == "on" exit %ERROR_CODE%
+
+exit /B %ERROR_CODE%

deploy/paddleOcrSpringBoot/paddleOcrSpringBoot.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module org.jetbrains.idea.maven.project.MavenProjectsManager.isMavenModule="true" type="JAVA_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="Spring" name="Spring">
+      <configuration />
+    </facet>
+    <facet type="web" name="Web">
+      <configuration>
+        <webroots />
+        <sourceRoots>
+          <root url="file://$MODULE_DIR$/src/main/java" />
+          <root url="file://$MODULE_DIR$/src/main/resources" />
+        </sourceRoots>
+      </configuration>
+    </facet>
+  </component>
+  <component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_1_8">
+    <output url="file://$MODULE_DIR$/target/classes" />
+    <output-test url="file://$MODULE_DIR$/target/test-classes" />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/test/java" isTestSource="true" />
+      <excludeFolder url="file://$MODULE_DIR$/target" />
+    </content>
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+    <orderEntry type="sourceFolder" forTests="false" />
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+    <orderEntry type="library" name="Maven: org.springframework.boot:spring-boot:2.3.4.RELEASE" level="project" />
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+    <orderEntry type="library" name="Maven: org.springframework:spring-beans:5.2.9.RELEASE" level="project" />
+    <orderEntry type="library" name="Maven: org.springframework:spring-webmvc:5.2.9.RELEASE" level="project" />
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+    <orderEntry type="library" scope="TEST" name="Maven: net.minidev:json-smart:2.3" level="project" />
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+    <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter:5.6.2" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-api:5.6.2" level="project" />
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+    <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-engine:5.6.2" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.junit.platform:junit-platform-engine:1.6.2" level="project" />
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+    <orderEntry type="library" scope="TEST" name="Maven: net.bytebuddy:byte-buddy:1.10.14" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: net.bytebuddy:byte-buddy-agent:1.10.14" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.objenesis:objenesis:2.6" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.mockito:mockito-junit-jupiter:3.3.3" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.skyscreamer:jsonassert:1.5.0" level="project" />
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+    <orderEntry type="library" name="Maven: org.springframework:spring-jcl:5.2.9.RELEASE" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.springframework:spring-test:5.2.9.RELEASE" level="project" />
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+    <orderEntry type="library" name="Maven: org.apache.httpcomponents:httpcore:4.4.13" level="project" />
+    <orderEntry type="library" name="Maven: commons-codec:commons-codec:1.14" level="project" />
+    <orderEntry type="library" name="Maven: com.vaadin.external.google:android-json:0.0.20131108.vaadin1" level="project" />
+    <orderEntry type="library" name="Maven: com.google.code.gson:gson:2.8.6" level="project" />
+  </component>
+</module>
\ No newline at end of file

deploy/paddleOcrSpringBoot/pom.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <parent>
+        <groupId>org.springframework.boot</groupId>
+        <artifactId>spring-boot-starter-parent</artifactId>
+        <version>2.3.4.RELEASE</version>
+        <relativePath/> <!-- lookup parent from repository -->
+    </parent>
+    <groupId>com.paddelOcr_springBoot</groupId>
+    <artifactId>demo</artifactId>
+    <version>0.0.1-SNAPSHOT</version>
+    <name>demo</name>
+    <description>Demo project for Spring Boot</description>
+
+    <properties>
+        <java.version>1.8</java.version>
+    </properties>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-web</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-test</artifactId>
+            <scope>test</scope>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.junit.vintage</groupId>
+                    <artifactId>junit-vintage-engine</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+        <dependency>
+            <!--引入RestTemplate-->
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>com.vaadin.external.google</groupId>
+            <artifactId>android-json</artifactId>
+            <version>0.0.20131108.vaadin1</version>
+            <scope>compile</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.google.code.gson</groupId>
+            <artifactId>gson</artifactId>
+            <version>2.8.6</version>
+        </dependency>
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.springframework.boot</groupId>
+                <artifactId>spring-boot-maven-plugin</artifactId>
+            </plugin>
+        </plugins>
+    </build>
+
+</project>

deploy/paddleOcrSpringBoot/readme.md

+简体中文
+
+- 使用本教程前请先基于PaddleHub Serving的部署.
+
+# 基于PaddleHub Serving的Java SpringBoot调用
+
+paddleOcrSpringBoot服务部署目录下包括全部SpringBoot代码。目录结构如下：
+```
+deploy/paddleOcrSpringBoot/
+  └─  src     代码文件
+       └─  main     主函数代码
+            └─  java\com\paddelocr_springboot\demo
+                                               └─ DemoApplication.java  SpringBoot启动代码
+                                               └─ Controller    
+                                                   └─ OCR.java    控制器代码
+       └─  test     测试代码
+```
+
+- Hub Serving启动后的APi端口如下：
+`http://[ip_address]:[port]/predict/[module_name]`  
+
+## 返回结果格式说明
+返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下：
+
+|字段名称|数据类型|意义|
+|-|-|-|
+|text|str|文本内容|
+|confidence|float| 文本识别置信度|
+|text_region|list|文本位置坐标|
+
+不同模块返回的字段不同，如，文本识别服务模块返回结果不含`text_region`字段，具体信息如下：
+
+|字段名/模块名|ocr_det|ocr_rec|ocr_system|
+|-|-|-|-|
+|text||✔|✔|
+|confidence||✔|✔|
+|text_region|✔||✔|
\ No newline at end of file

deploy/paddleOcrSpringBoot/src/main/java/com/paddelocr_springboot/demo/Controller/OCR.java

+package com.paddelocr_springboot.demo.Controller;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import com.google.gson.Gson;
+import com.google.gson.JsonElement;
+import com.google.gson.JsonObject;
+import com.google.gson.JsonParser;
+import org.json.JSONObject;
+import org.springframework.util.LinkedMultiValueMap;
+import org.springframework.util.ResourceUtils;
+import org.springframework.web.bind.annotation.RequestMapping;
+import org.springframework.web.bind.annotation.RestController;
+import org.springframework.http.*;
+import org.springframework.util.MultiValueMap;
+import org.springframework.web.client.RestTemplate;
+import sun.misc.BASE64Encoder;
+import java.util.Objects;
+
+@RestController
+public class OCR {
+@RequestMapping("/")
+    public ResponseEntity<String> hi(){
+        //创建请求头
+        HttpHeaders headers = new HttpHeaders();
+        //设置请求头格式
+        headers.setContentType(MediaType.APPLICATION_JSON);
+        //读入静态资源文件1.png
+        InputStream imagePath = this.getClass().getResourceAsStream("/1.png");
+        //构建请求参数
+        MultiValueMap<String, String> map= new LinkedMultiValueMap<String, String>();
+        //添加请求参数images，并将Base64编码的图片传入
+        map.add("images", ImageToBase64(imagePath));
+        //构建请求
+        HttpEntity<MultiValueMap<String, String>> request = new HttpEntity<MultiValueMap<String, String>>(map, headers);
+        RestTemplate restTemplate = new RestTemplate();
+        //发送请求
+        ResponseEntity<String> response = restTemplate.postForEntity("http://127.0.0.1:8866/predict/ocr_system", request, String.class);
+        //打印请求返回值
+        return response;
+    }
+    private String ImageToBase64(InputStream imgPath) {
+        byte[] data = null;
+        // 读取图片字节数组
+        try {
+            InputStream in = imgPath;
+            System.out.println(imgPath);
+            data = new byte[in.available()];
+            in.read(data);
+            in.close();
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+        // 对字节数组Base64编码
+        BASE64Encoder encoder = new BASE64Encoder();
+        // 返回Base64编码过的字节数组字符串
+        //System.out.println("图片转换Base64:" + encoder.encode(Objects.requireNonNull(data)));
+        return encoder.encode(Objects.requireNonNull(data));
+    }
+
+}

deploy/paddleOcrSpringBoot/src/main/java/com/paddelocr_springboot/demo/DemoApplication.java

+package com.paddelocr_springboot.demo;
+
+import org.springframework.boot.SpringApplication;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+
+@SpringBootApplication
+public class DemoApplication {
+
+    public static void main(String[] args) {
+        SpringApplication.run(DemoApplication.class, args);
+    }
+
+}

deploy/paddleOcrSpringBoot/src/main/resources/application.properties

+server.port=8081
+http_pool.max_total: 200
+http_pool.default_max_per_route: 100
+http_pool.connect_timeout: 5000
+http_pool.connection_request_timeout: 1000
+http_pool.socket_timeout: 65000
+http_pool.validate_after_inactivity: 2000
\ No newline at end of file

deploy/paddleOcrSpringBoot/src/test/java/com/paddelocr_springboot/demo/DemoApplicationTests.java

+package com.paddelocr_springboot.demo;
+
+import org.junit.jupiter.api.Test;
+import org.springframework.boot.test.context.SpringBootTest;
+
+@SpringBootTest
+class DemoApplicationTests {
+
+    @Test
+    void contextLoads() {
+    }
+
+}

deploy/pdserving/ocr_local_server.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from paddle_serving_client import Client
-from paddle_serving_app.reader import OCRReader
 import cv2
 import sys
 import numpy as np

deploy/pdserving/ocr_rpc_server.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from paddle_serving_client import Client
-from paddle_serving_app.reader import OCRReader
 import cv2
 import sys
 import numpy as np
@@ -59,7 +58,6 @@ class TextSystemHelper(TextSystem):
         fetch_map = self.det_client.predict(feed, fetch)
         outputs = [fetch_map[x] for x in fetch]
         dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
-        # print(dt_boxes)
         if dt_boxes is None:
             return None, None
         img_crop_list = []
@@ -73,7 +71,6 @@ class TextSystemHelper(TextSystem):
             feed, fetch, self.tmp_args = self.text_classifier.preprocess(
                 img_crop_list)
             fetch_map = self.clas_client.predict(feed, fetch)
-            # print(fetch_map)
             outputs = [fetch_map[x] for x in self.text_classifier.fetch]
             for x in fetch_map.keys():
                 if ".lod" in x:

deploy/slim/quantization/export_model.py

@@ -51,6 +51,7 @@ from paddleslim.quant import quant_aware, convert
 from paddle.fluid.layer_helper import LayerHelper
 from eval_utils.eval_det_utils import eval_det_run
 from eval_utils.eval_rec_utils import eval_rec_run
+from eval_utils.eval_cls_utils import eval_cls_run
 
 
 def main():
@@ -105,6 +106,8 @@ def main():
 
     if alg_type == 'det':
         final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
+    elif alg_type == 'cls':
+        final_metrics = eval_cls_run(exe, quant_info_dict)
     else:
         final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
     print(final_metrics)

deploy/slim/quantization/quant.py

@@ -178,9 +178,12 @@ def main():
     if train_alg_type == 'det':
         program.train_eval_det_run(
             config, exe, train_info_dict, eval_info_dict, is_slim="quant")
-    else:
+    elif train_alg_type == 'rec':
         program.train_eval_rec_run(
             config, exe, train_info_dict, eval_info_dict, is_slim="quant")
+    else:
+        program.train_eval_cls_run(
+            config, exe, train_info_dict, eval_info_dict, is_slim="quant")
 
 
 if __name__ == '__main__':

doc/doc_ch/FAQ.md

@@ -9,43 +9,45 @@
 
 ## PaddleOCR常见问题汇总(持续更新)
 
-* [近期更新（2020.10.19）](#近期更新)
+* [近期更新（2020.10.26）](#近期更新)
 * [【精选】OCR精选10个问题](#OCR精选10个问题)
-* [【理论篇】OCR通用21个问题](#OCR通用问题)
-  * [基础知识3题](#基础知识)
+* [【理论篇】OCR通用23个问题](#OCR通用问题)
+  * [基础知识5题](#基础知识)
   * [数据集4题](#数据集)
   * [模型训练调优6题](#模型训练调优)
   * [预测部署8题](#预测部署)
-* [【实战篇】PaddleOCR实战58个问题](#PaddleOCR实战问题)
-  * [使用咨询17题](#使用咨询)
+* [【实战篇】PaddleOCR实战61个问题](#PaddleOCR实战问题)
+  * [使用咨询20题](#使用咨询)
   * [数据集10题](#数据集)
   * [模型训练调优15题](#模型训练调优)
   * [预测部署16题](#预测部署)
 
 
 <a name="近期更新"></a>
-## 近期更新（2020.10.19）
+## 近期更新（2020.10.26）
 
-#### Q3.3.14：使用之前版本的代码加载最新1.1版的通用检测预训练模型，提示在模型文件.pdparams中找不到bn4e_branch2a_variance是什么情况？是网络结构发生了变化吗？
-
-**A**：1.1版的轻量检测模型去掉了mv3结构中的se模块，可以对比下这两个配置文件：[det_mv3_db.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/configs/det/det_mv3_db.yml)，[det_mv3_db_v1.1.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/configs/det/det_mv3_db_v1.1.yml)
+#### Q2.1.4 印章如何识别
+**A**: 1. 使用带tps的识别网络或abcnet,2.使用极坐标变换将图片拉平之后使用crnn
 
-#### Q3.3.15： 训练中使用的字典需要与加载的预训练模型使用的字典一样吗？
-
-**A**：是的，训练的字典与你使用该模型进行预测的字典需要保持一致的。
+#### Q2.1.5 多语言的字典里是混合了不同的语种，这个是有什么讲究吗？统一到一个字典里会对精度造成多大的损失？
+**A**：统一到一个字典里，会造成最后一层FC过大，增加模型大小。如果有特殊需求的话，可以把需要的几种语言合并字典训练模型，合并字典之后如果引入过多的形近字，可能会造成精度损失，字符平衡的问题可能也需要考虑一下。在PaddleOCR里暂时将语言字典分开。
 
-#### Q3.2.10： crnn+ctc模型训练所用的垂直文本（旋转至水平方向）是如何生成的？
+#### Q3.3.16: 如何对检测模型finetune，比如冻结前面的层或某些层使用小的学习率学习？
 
-**A**：方法与合成水平方向文字一致，只是将字体替换成了垂直字体。
-
-#### Q3.4.15： hubserving、pdserving这两种部署方式区别是什么？
-
-**A**：hubserving原本是paddlehub的配套服务部署工具，可以很方便的将paddlehub内置的模型部署为服务，paddleocr使用了这个功能，并将模型路径等参数暴露出来方便用户自定义修改。paddle serving是面向所有paddle模型的部署工具，文档中可以看到我们提供了快速版和标准版，其中快速版和hubserving的本质是一样的，而标准版基于rpc，更稳定，更适合分布式部署。
+**A**：如果是冻结某些层，可以将变量的stop_gradient属性设置为True，这样计算这个变量之前的所有参数都不会更新了，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/faq/train_cn.html#id4
+如果对某些层使用更小的学习率学习，静态图里还不是很方便，一个方法是在参数初始化的时候，给权重的属性设置固定的学习率，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr
+实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错
 
-#### Q3.4.16： hub serving部署服务时如何多gpu同时利用起来，export CUDA_VISIBLE_DEVICES=0,1 方式吗？
+#### Q3.3.17: 使用通用中文模型作为预训练模型，更改了字典文件，出现ctc_fc_b not used的错误
+**A**：修改了字典之后，识别模型的最后一层FC纬度发生了改变，没有办法加载参数。这里是一个警告，可以忽略，正常训练即可。
 
-**A**：hubserving的部署方式目前暂不支持多卡预测，除非手动启动多个serving，不同端口对应不同卡。或者可以使用paddleserving进行部署，部署工具已经发布：https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/pdserving ，在启动服务时--gpu_id 0,1 这样就可以
 
+#### Q3.1.18：如何加入自己的检测算法？
+**A**：1. 在ppocr/modeling对应目录下分别选择backbone，head。如果没有可用的可以新建文件并添加
+       2. 在ppocr/data下选择对应的数据处理处理方式，如果没有可用的可以新建文件并添加
+       3. 在ppocr/losses下新建文件并编写loss
+       4. 在ppocr/postprocess下新建文件并编写后处理算法
+       5. 将上面四个步骤里新添加的类或函数参照yml文件写到配置中
 
 <a name="OCR精选10个问题"></a>
 ## 【精选】OCR精选10个问题
@@ -151,6 +153,11 @@
 
 **A**：端到端在文字分布密集的业务场景，效率会比较有保证，精度的话看自己业务数据积累情况，如果行级别的识别数据积累比较多的话two-stage会比较好。百度的落地场景，比如工业仪表识别、车牌识别都用到端到端解决方案。
 
+#### Q2.1.4 印章如何识别
+**A**: 1. 使用带tps的识别网络或abcnet,2.使用极坐标变换将图片拉平之后使用crnn
+
+#### Q2.1.5 多语言的字典里是混合了不同的语种，这个是有什么讲究吗？统一到一个字典里会对精度造成多大的损失？
+**A**：统一到一个字典里，会造成最后一层FC过大，增加模型大小。如果有特殊需求的话，可以把需要的几种语言合并字典训练模型，合并字典之后如果引入过多的形近字，可能会造成精度损失，字符平衡的问题可能也需要考虑一下。在PaddleOCR里暂时将语言字典分开。
 
 ### 数据集
 
@@ -329,6 +336,13 @@
 |8.6M超轻量中文OCR模型|MobileNetV3+MobileNetV3|det_mv3_db.yml|rec_chinese_lite_train.yml|
 |通用中文OCR模型|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml|
 
+#### 3.1.18：如何加入自己的检测算法？
+**A**：1. 在ppocr/modeling对应目录下分别选择backbone，head。如果没有可用的可以新建文件并添加
+       2. 在ppocr/data下选择对应的数据处理处理方式，如果没有可用的可以新建文件并添加
+       3. 在ppocr/losses下新建文件并编写loss
+       4. 在ppocr/postprocess下新建文件并编写后处理算法
+       5. 将上面四个步骤里新添加的类或函数参照yml文件写到配置中
+
 
 ### 数据集
 
@@ -388,6 +402,7 @@
 
 **A**：方法与合成水平方向文字一致，只是将字体替换成了垂直字体。
 
+
 ### 模型训练调优
 
 #### Q3.3.1：文本长度超过25，应该怎么处理？
@@ -463,7 +478,20 @@ return paddle.reader.multiprocess_reader(readers, False, queue_size=320)
 
 #### Q3.3.15： 训练中使用的字典需要与加载的预训练模型使用的字典一样吗？
 
-**A**：是的，训练的字典与你使用该模型进行预测的字典需要保持一致的。
+**A**：分情况，1. 不改变识别字符，训练的字典与你使用该模型进行预测的字典需要保持一致的。
+             2. 改变识别的字符，这种情况可以不一样，最后一层会重新训练
+
+#### Q3.3.16: 如何对检测模型finetune，比如冻结前面的层或某些层使用小的学习率学习？
+**A**：
+
+**A**：如果是冻结某些层，可以将变量的stop_gradient属性设置为True，这样计算这个变量之前的所有参数都不会更新了，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/faq/train_cn.html#id4
+
+如果对某些层使用更小的学习率学习，静态图里还不是很方便，一个方法是在参数初始化的时候，给权重的属性设置固定的学习率，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr
+
+实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错
+
+#### Q3.3.17: 使用通用中文模型作为预训练模型，更改了字典文件，出现ctc_fc_b not used的错误
+**A**：修改了字典之后，识别模型的最后一层FC纬度发生了改变，没有办法加载参数。这里是一个警告，可以忽略，正常训练即可。
 
 ### 预测部署
 

doc/doc_ch/installation.md

@@ -47,9 +47,9 @@ docker images
 hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
 ```
 
-**2. 安装PaddlePaddle Fluid v2.0**
+**2. 安装PaddlePaddle v2.0**
 ```
-pip3 install --upgrade pip
+python3 -m pip install --upgrade pip
 
 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
 python3 -m pip install paddlepaddle-gpu==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
@@ -75,7 +75,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 **4. 安装第三方库**
 ```
 cd PaddleOCR
-pip3 install -r requirments.txt
+python3 -m pip install -r requirments.txt
 ```
 
 注意，windows环境下，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装，

doc/doc_ch/quickstart.md

@@ -95,5 +95,5 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_mode
 
 此外，文档教程中也提供了中文OCR模型的其他预测部署方式：
 - [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
-- [服务部署](../../deploy/pdserving/readme.md)
+- [服务部署](./serving_inference.md)
 - [端侧部署](../../deploy/lite/readme.md)

doc/doc_ch/recognition.md

@@ -120,19 +120,19 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 
 `ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典，
 
-`ppocr/utils/french_dict.txt` 是一个包含118个字符的法文字典
+`ppocr/utils/dict/french_dict.txt` 是一个包含118个字符的法文字典
 
-`ppocr/utils/japan_dict.txt` 是一个包含4399个字符的法文字典
+`ppocr/utils/dict/japan_dict.txt` 是一个包含4399个字符的法文字典
 
-`ppocr/utils/korean_dict.txt` 是一个包含3636个字符的法文字典
+`ppocr/utils/dict/korean_dict.txt` 是一个包含3636个字符的法文字典
 
-`ppocr/utils/german_dict.txt` 是一个包含131个字符的法文字典
+`ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的法文字典
 
 
 您可以按需使用。
 
 目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**，
-如您愿意可将字典文件提交至 [utils](../../ppocr/utils) ，我们会在Repo中感谢您。
+如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict) 将语料文件提交至[corpus](../../ppocr/utils/corpus)，我们会在Repo中感谢您。
 
 - 自定义字典
 
@@ -269,7 +269,7 @@ PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的
 Global:
   ...
   # 添加自定义字典，如修改字典请将路径指向新字典
-  character_dict_path: ./ppocr/utils/french_dict.txt
+  character_dict_path: ./ppocr/utils/dict/french_dict.txt
   # 训练时添加数据增强
   distort: true
   # 识别空格

doc/doc_ch/serving_inference.md

+PaddleOCR提供2种服务部署方式：
+- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。。
+- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../../deploy/hubserving/readme.md)
+
 # 使用Paddle Serving预测推理
 
 阅读本文档之前，请先阅读文档 [基于Python预测引擎推理](./inference.md)
@@ -8,7 +12,7 @@
 
 ### 一、 准备环境
 我们先安装Paddle Serving相关组件
-我们推荐用户使用GPU来做Paddle Serving的OCR服务部署 
+我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
 
 **CUDA版本：9.X/10.X**
 
@@ -26,7 +30,7 @@
 #CPU/GPU版本选择一个
 #GPU版本服务端
 #CUDA 9
-python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl 
+python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl
 #CUDA 10
 python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py3-none-any.whl
 #CPU版本服务端
@@ -81,7 +85,7 @@ def read_params():
     #params for text detector
     cfg.det_algorithm = "DB" # 检测算法， DB/EAST等
     cfg.det_model_dir = "./det_mv_server/" # 检测算法模型路径
-    cfg.det_max_side_len = 960 
+    cfg.det_max_side_len = 960
 
     #DB params
     cfg.det_db_thresh =0.3
@@ -222,14 +226,14 @@ python rec_web_client.py
 #GPU用户
 python -m paddle_serving_server_gpu.serve --model det_infer_server --port 9293 --gpu_id 0
 python -m paddle_serving_server_gpu.serve --model cls_infer_server --port 9294 --gpu_id 0
-python ocr_rpc_server.py 
+python ocr_rpc_server.py
 #CPU用户
 python -m paddle_serving_server.serve --model det_infer_server --port 9293
 python -m paddle_serving_server.serve --model cls_infer_server --port 9294
 python ocr_rpc_server.py
 
 #快速版，Windows/Linux用户
-python ocr_local_server.py 
+python ocr_local_server.py
 ```
 
 客户端

doc/doc_en/angle_class_en.md

@@ -110,7 +110,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci
 
 ```
 # Predict English results
-python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
 ```
 
 Input image:

doc/doc_en/inference_en.md

@@ -330,7 +330,7 @@ If you need to predict other language models, when using inference model predict
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition:
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/ utils/korean_dict.txt" --vis_font_path="doc/korean.ttf"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
 

doc/doc_en/installation_en.md

@@ -18,7 +18,7 @@ cd /home/Projects
 # You need to create a docker container for the first run, and do not need to run the current command when you run it again
 # Create a docker container named ppocr and map the current directory to the /paddle directory of the container
 
-#If using CPU, use docker instead of nvidia-docker to create docker
+# If using CPU, use docker instead of nvidia-docker to create docker
 sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
 ```
 If using CUDA9, please run the following command to create a container:
@@ -49,9 +49,9 @@ docker images
 hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
 ```
 
-**2. Install PaddlePaddle Fluid v2.0**
+**2. Install PaddlePaddle v2.0**
 ```
-pip3 install --upgrade pip
+python3 -m pip install --upgrade pip
 
 # If you have cuda9 or cuda10 installed on your machine, please run the following command to install
 python3 -m pip install paddlepaddle-gpu==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
@@ -77,7 +77,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 **4. Install third-party libraries**
 ```
 cd PaddleOCR
-pip3 install -r requirments.txt
+python3 -m pip install -r requirments.txt
 ```
 
 If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.

doc/doc_en/quickstart_en.md

@@ -98,5 +98,5 @@ For more text detection and recognition tandem reasoning, please refer to the do
 
 In addition, the tutorial also provides other deployment methods for the Chinese OCR model:
 - [Server-side C++ inference](../../deploy/cpp_infer/readme_en.md)
-- [Service deployment](../../deploy/pdserving/readme_en.md)
+- [Service deployment](../../deploy/hubserving/readme_en.md)
 - [End-to-end deployment](../../deploy/lite/readme_en.md)

doc/doc_en/recognition_en.md

@@ -112,18 +112,18 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
 
 `ppocr/utils/ic15_dict.txt` is an English dictionary with 63 characters
 
-`ppocr/utils/french_dict.txt` is a French dictionary with 118 characters
+`ppocr/utils/dict/french_dict.txt` is a French dictionary with 118 characters
 
-`ppocr/utils/japan_dict.txt` is a French dictionary with 4399 characters
+`ppocr/utils/dict/japan_dict.txt` is a French dictionary with 4399 characters
 
-`ppocr/utils/korean_dict.txt` is a French dictionary with 3636 characters
+`ppocr/utils/dict/korean_dict.txt` is a French dictionary with 3636 characters
 
-`ppocr/utils/german_dict.txt` is a French dictionary with 131 characters
+`ppocr/utils/dict/german_dict.txt` is a French dictionary with 131 characters
 
 You can use it on demand.
 
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
-If you like, you can submit the dictionary file to [utils](../../ppocr/utils) and we will thank you in the Repo.
+If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) or corpus file to [corpus](../../ppocr/utils/corpus) and we will thank you in the Repo.
 
 
 To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
@@ -259,7 +259,7 @@ Global:
   ...
   # Add a custom dictionary, if you modify the dictionary
   # please point the path to the new dictionary
-  character_dict_path: ./ppocr/utils/french_dict.txt
+  character_dict_path: ./ppocr/utils/dict/french_dict.txt
   # Add data augmentation during training
   distort: true
   # Identify spaces

ppocr/data/cls/dataset_traversal.py

@@ -74,7 +74,7 @@ class SimpleReader(object):
 
         def get_device_num():
             if self.use_gpu:
-                gpus = os.environ.get("CUDA_VISIBLE_DEVICES", 1)
+                gpus = os.environ.get("CUDA_VISIBLE_DEVICES", "1")
                 gpu_num = len(gpus.split(','))
                 return gpu_num
             else:

ppocr/data/det/db_process.py

@@ -28,14 +28,22 @@ from .make_border_map import MakeBorderMap
 
 class DBProcessTrain(object):
     """
-    DB pre-process for Train mode
+    The pre-process of DB for train mode
     """
 
     def __init__(self, params):
+        """
+        :param params: dict of params
+        """
         self.img_set_dir = params['img_set_dir']
         self.image_shape = params['image_shape']
 
     def order_points_clockwise(self, pts):
+        """
+        Sort the points in the box clockwise
+        :param pts: points with shape [4, 2]
+        :return: sorted points
+        """
         rect = np.zeros((4, 2), dtype="float32")
         s = pts.sum(axis=1)
         rect[0] = pts[np.argmin(s)]
@@ -46,6 +54,12 @@ class DBProcessTrain(object):
         return rect
 
     def make_data_dict(self, imgvalue, entry):
+        """
+        create input dict
+        :param imgvalue: input image
+        :param entry: dict of annotations information
+        :return: created dict of input data information
+        """
         boxes = []
         texts = []
         ignores = []
@@ -71,6 +85,11 @@ class DBProcessTrain(object):
         return data
 
     def NormalizeImage(self, data):
+        """
+        Normalize input image
+        :param data: input dict
+        :return: new dict with normalized image
+        """
         im = data['image']
         img_mean = [0.485, 0.456, 0.406]
         img_std = [0.229, 0.224, 0.225]
@@ -84,6 +103,11 @@ class DBProcessTrain(object):
         return data
 
     def FilterKeys(self, data):
+        """
+        Filter keys
+        :param data: dict
+        :return:
+        """
         filter_keys = ['polys', 'texts', 'ignore_tags', 'shape']
         for key in filter_keys:
             if key in data:
@@ -91,6 +115,11 @@ class DBProcessTrain(object):
         return data
 
     def convert_label_infor(self, label_infor):
+        """
+        encode annotations using json.loads
+        :param label_infor: string
+        :return: (image, encoded annotations)
+        """
         label_infor = label_infor.decode()
         label_infor = label_infor.encode('utf-8').decode('utf-8-sig')
         substr = label_infor.strip("\n").split("\t")
@@ -165,13 +194,13 @@ class DBProcessTest(object):
         elif resize_h // 32 <= 1:
             resize_h = 32
         else:
-            resize_h = (resize_h // 32 - 1) * 32
+            resize_h = (resize_h // 32) * 32
         if resize_w % 32 == 0:
             resize_w = resize_w
         elif resize_w // 32 <= 1:
             resize_w = 32
         else:
-            resize_w = (resize_w // 32 - 1) * 32
+            resize_w = (resize_w // 32) * 32
         try:
             if int(resize_w) <= 0 or int(resize_h) <= 0:
                 return None, (None, None)
@@ -184,6 +213,11 @@ class DBProcessTest(object):
         return im, (ratio_h, ratio_w)
 
     def resize_image_type1(self, im):
+        """
+        resize image to a size self.image_shape
+        :param im: input image
+        :return: normalized image and resize ratio
+        """
         resize_h, resize_w = self.image_shape
         ori_h, ori_w = im.shape[:2]  # (h, w, c)
         im = cv2.resize(im, (int(resize_w), int(resize_h)))
@@ -192,6 +226,11 @@ class DBProcessTest(object):
         return im, (ratio_h, ratio_w)
 
     def normalize(self, im):
+        """
+        Normalize image
+        :param im: input image
+        :return: Normalized image
+        """
         img_mean = [0.485, 0.456, 0.406]
         img_std = [0.229, 0.224, 0.225]
         im = im.astype(np.float32, copy=False)

ppocr/data/det/east_process.py

@@ -19,6 +19,7 @@ import json
 import sys
 import os
 
+
 class EASTProcessTrain(object):
     def __init__(self, params):
         self.img_set_dir = params['img_set_dir']
@@ -495,13 +496,13 @@ class EASTProcessTest(object):
         elif resize_h // 32 <= 1:
             resize_h = 32
         else:
-            resize_h = (resize_h // 32 - 1) * 32
+            resize_h = (resize_h // 32) * 32
         if resize_w % 32 == 0:
             resize_w = resize_w
         elif resize_w // 32 <= 1:
             resize_w = 32
         else:
-            resize_w = (resize_w // 32 - 1) * 32
+            resize_w = (resize_w // 32) * 32
         try:
             if int(resize_w) <= 0 or int(resize_h) <= 0:
                 return None, (None, None)

ppocr/data/det/sast_process.py

@@ -599,7 +599,7 @@ class SASTProcessTrain(object):
         """
         text_polys, txt_tags, txts = [], [], []
 
-        with open(poly_txt_path) as f:
+        with open(poly_txt_path, 'rb') as f:
             for line in f.readlines():
                 poly_str, txt = line.strip().split('\t')
                 poly = map(float, poly_str.split(','))

ppocr/data/rec/img_tools.py

@@ -56,7 +56,7 @@ def resize_norm_img(img, image_shape):
 def resize_norm_img_chinese(img, image_shape):
     imgC, imgH, imgW = image_shape
     # todo: change to 0 and modified image shape
-    max_wh_ratio = 0
+    max_wh_ratio = imgW * 1.0 / imgH
     h, w = img.shape[0], img.shape[1]
     ratio = w * 1.0 / h
     max_wh_ratio = max(max_wh_ratio, ratio)
@@ -309,16 +309,28 @@ def warp(img, ang):
     if config.distort:
         img_height, img_width = img.shape[0:2]
         if random.random() <= prob and img_height >= 20 and img_width >= 20:
-            new_img = tia_distort(new_img, random.randint(3, 6))
+            try:
+                new_img = tia_distort(new_img, random.randint(3, 6))
+            except:
+                logger.warning(
+                    "Exception occured during tia_distort, pass it...")
 
     if config.stretch:
         img_height, img_width = img.shape[0:2]
         if random.random() <= prob and img_height >= 20 and img_width >= 20:
-            new_img = tia_stretch(new_img, random.randint(3, 6))
+            try:
+                new_img = tia_stretch(new_img, random.randint(3, 6))
+            except:
+                logger.warning(
+                    "Exception occured during tia_stretch, pass it...")
 
     if config.perspective:
         if random.random() <= prob:
-            new_img = tia_perspective(new_img)
+            try:
+                new_img = tia_perspective(new_img)
+            except:
+                logger.warning(
+                    "Exception occured during tia_perspective, pass it...")
 
     if config.crop:
         img_height, img_width = img.shape[0:2]

ppocr/modeling/architectures/cls_model.py

@@ -65,6 +65,7 @@ class ClsModel(object):
             labels = None
             loader = None
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
+        image.stop_gradient = False
         return image, labels, loader
 
     def __call__(self, mode):

ppocr/modeling/architectures/rec_model.py

@@ -16,6 +16,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from collections import OrderedDict
+
 from paddle import fluid
 
 from ppocr.utils.utility import create_module
@@ -215,16 +217,15 @@ class RecModel(object):
                 label = labels['label']
             if self.loss_type == 'srn':
                 total_loss, img_loss, word_loss = self.loss(predicts, labels)
-                outputs = {
-                    'total_loss': total_loss,
-                    'img_loss': img_loss,
-                    'word_loss': word_loss,
-                    'decoded_out': decoded_out,
-                    'label': label
-                }
+                outputs = OrderedDict([('total_loss', total_loss), 
+                                       ('img_loss', img_loss), 
+                                       ('word_loss', word_loss), 
+                                       ('decoded_out', decoded_out),
+                                       ('label', label)])
             else:
-                outputs = {'total_loss':loss, 'decoded_out':\
-                    decoded_out, 'label':label}
+                outputs = OrderedDict([('total_loss', loss), 
+                                       ('decoded_out', decoded_out),
+                                       ('label', label)])
             return loader, outputs
         # export_model
         elif mode == "export":
@@ -233,16 +234,15 @@ class RecModel(object):
                 predict = fluid.layers.softmax(predict)
             if self.loss_type == "srn":
                 return [
-                    image, labels, {
-                        'decoded_out': decoded_out,
-                        'predicts': predict
-                    }
-                ]
+                    image, labels, OrderedDict([('decoded_out', decoded_out), 
+                                                ('predicts', predict)])]
 
-            return [image, {'decoded_out': decoded_out, 'predicts': predict}]
+            return [image, OrderedDict([('decoded_out', decoded_out), 
+                                        ('predicts', predict)])]
         # eval or test
         else:
             predict = predicts['predict']
             if self.loss_type == "ctc":
                 predict = fluid.layers.softmax(predict)
-            return loader, {'decoded_out': decoded_out, 'predicts': predict}
+            return loader, OrderedDict([('decoded_out', decoded_out), 
+                                        ('predicts', predict)])
\ No newline at end of file

ppocr/modeling/heads/cls_head.py

@@ -23,6 +23,14 @@ import paddle.fluid as fluid
 
 
 class ClsHead(object):
+    """
+    Class orientation
+
+    Args:
+
+        params(dict): super parameters for build Class network
+    """
+
     def __init__(self, params):
         super(ClsHead, self).__init__()
         self.class_dim = params['class_dim']

ppocr/modeling/heads/det_east_head.py

@@ -109,6 +109,12 @@ class EASTHead(object):
         return f_score, f_geo
 
     def __call__(self, inputs):
+        """
+        Fuse different levels of feature map from backbone and predict results
+        Args:
+            inputs(list): feature maps from backbone
+        Return: predicts
+        """
         f_common = self.unet_fusion(inputs)
         f_score, f_geo = self.detector_header(f_common)
         predicts = OrderedDict()

ppocr/modeling/heads/det_sast_head.py

@@ -38,35 +38,66 @@ class SASTHead(object):
         blocks{}: contain block_2, block_3, block_4, block_5, block_6, block_7 with
                 1/4, 1/8, 1/16, 1/32, 1/64, 1/128 resolution.
         """
-        f = [blocks['block_6'], blocks['block_5'], blocks['block_4'], blocks['block_3'], blocks['block_2']]
+        f = [
+            blocks['block_6'], blocks['block_5'], blocks['block_4'],
+            blocks['block_3'], blocks['block_2']
+        ]
         num_outputs = [256, 256, 192, 192, 128]
         g = [None, None, None, None, None]
-        h = [None, None, None, None, None] 
+        h = [None, None, None, None, None]
         for i in range(5):
-            h[i] = conv_bn_layer(input=f[i], num_filters=num_outputs[i],
-                                filter_size=1, stride=1, act=None, name='fpn_up_h'+str(i))
+            h[i] = conv_bn_layer(
+                input=f[i],
+                num_filters=num_outputs[i],
+                filter_size=1,
+                stride=1,
+                act=None,
+                name='fpn_up_h' + str(i))
 
         for i in range(4):
             if i == 0:
-                g[i] = deconv_bn_layer(input=h[i], num_filters=num_outputs[i + 1], act=None, name='fpn_up_g0')
+                g[i] = deconv_bn_layer(
+                    input=h[i],
+                    num_filters=num_outputs[i + 1],
+                    act=None,
+                    name='fpn_up_g0')
                 #print("g[{}] shape: {}".format(i, g[i].shape))
             else:
                 g[i] = fluid.layers.elementwise_add(x=g[i - 1], y=h[i])
                 g[i] = fluid.layers.relu(g[i])
                 #g[i] = conv_bn_layer(input=g[i], num_filters=num_outputs[i],
                 #                    filter_size=1, stride=1, act='relu')
-                g[i] = conv_bn_layer(input=g[i], num_filters=num_outputs[i],
-                                    filter_size=3, stride=1, act='relu', name='fpn_up_g%d_1'%i)
-                g[i] = deconv_bn_layer(input=g[i], num_filters=num_outputs[i + 1], act=None, name='fpn_up_g%d_2'%i)
+                g[i] = conv_bn_layer(
+                    input=g[i],
+                    num_filters=num_outputs[i],
+                    filter_size=3,
+                    stride=1,
+                    act='relu',
+                    name='fpn_up_g%d_1' % i)
+                g[i] = deconv_bn_layer(
+                    input=g[i],
+                    num_filters=num_outputs[i + 1],
+                    act=None,
+                    name='fpn_up_g%d_2' % i)
                 #print("g[{}] shape: {}".format(i, g[i].shape))
 
         g[4] = fluid.layers.elementwise_add(x=g[3], y=h[4])
         g[4] = fluid.layers.relu(g[4])
-        g[4] = conv_bn_layer(input=g[4], num_filters=num_outputs[4],
-                            filter_size=3, stride=1, act='relu', name='fpn_up_fusion_1')
-        g[4] = conv_bn_layer(input=g[4], num_filters=num_outputs[4],
-                            filter_size=1, stride=1, act=None, name='fpn_up_fusion_2')
-        
+        g[4] = conv_bn_layer(
+            input=g[4],
+            num_filters=num_outputs[4],
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='fpn_up_fusion_1')
+        g[4] = conv_bn_layer(
+            input=g[4],
+            num_filters=num_outputs[4],
+            filter_size=1,
+            stride=1,
+            act=None,
+            name='fpn_up_fusion_2')
+
         return g[4]
 
     def FPN_Down_Fusion(self, blocks):
@@ -77,95 +108,245 @@ class SASTHead(object):
         f = [blocks['block_0'], blocks['block_1'], blocks['block_2']]
         num_outputs = [32, 64, 128]
         g = [None, None, None]
-        h = [None, None, None] 
+        h = [None, None, None]
         for i in range(3):
-            h[i] = conv_bn_layer(input=f[i], num_filters=num_outputs[i],
-                                filter_size=3, stride=1, act=None, name='fpn_down_h'+str(i))
+            h[i] = conv_bn_layer(
+                input=f[i],
+                num_filters=num_outputs[i],
+                filter_size=3,
+                stride=1,
+                act=None,
+                name='fpn_down_h' + str(i))
         for i in range(2):
             if i == 0:
-                g[i] = conv_bn_layer(input=h[i], num_filters=num_outputs[i+1], filter_size=3, stride=2, act=None, name='fpn_down_g0')
+                g[i] = conv_bn_layer(
+                    input=h[i],
+                    num_filters=num_outputs[i + 1],
+                    filter_size=3,
+                    stride=2,
+                    act=None,
+                    name='fpn_down_g0')
             else:
                 g[i] = fluid.layers.elementwise_add(x=g[i - 1], y=h[i])
                 g[i] = fluid.layers.relu(g[i])
-                g[i] = conv_bn_layer(input=g[i], num_filters=num_outputs[i], filter_size=3, stride=1, act='relu', name='fpn_down_g%d_1'%i)
-                g[i] = conv_bn_layer(input=g[i], num_filters=num_outputs[i+1], filter_size=3, stride=2, act=None, name='fpn_down_g%d_2'%i)
+                g[i] = conv_bn_layer(
+                    input=g[i],
+                    num_filters=num_outputs[i],
+                    filter_size=3,
+                    stride=1,
+                    act='relu',
+                    name='fpn_down_g%d_1' % i)
+                g[i] = conv_bn_layer(
+                    input=g[i],
+                    num_filters=num_outputs[i + 1],
+                    filter_size=3,
+                    stride=2,
+                    act=None,
+                    name='fpn_down_g%d_2' % i)
             # print("g[{}] shape: {}".format(i, g[i].shape)) 
         g[2] = fluid.layers.elementwise_add(x=g[1], y=h[2])
         g[2] = fluid.layers.relu(g[2])
-        g[2] = conv_bn_layer(input=g[2], num_filters=num_outputs[2],
-                            filter_size=3, stride=1, act='relu', name='fpn_down_fusion_1')
-        g[2] = conv_bn_layer(input=g[2], num_filters=num_outputs[2],
-                            filter_size=1, stride=1, act=None, name='fpn_down_fusion_2')
+        g[2] = conv_bn_layer(
+            input=g[2],
+            num_filters=num_outputs[2],
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='fpn_down_fusion_1')
+        g[2] = conv_bn_layer(
+            input=g[2],
+            num_filters=num_outputs[2],
+            filter_size=1,
+            stride=1,
+            act=None,
+            name='fpn_down_fusion_2')
         return g[2]
 
     def SAST_Header1(self, f_common):
         """Detector header."""
         #f_score
-        f_score = conv_bn_layer(input=f_common, num_filters=64, filter_size=1, stride=1, act='relu', name='f_score1')
-        f_score = conv_bn_layer(input=f_score, num_filters=64, filter_size=3, stride=1, act='relu', name='f_score2')
-        f_score = conv_bn_layer(input=f_score, num_filters=128, filter_size=1, stride=1, act='relu', name='f_score3')
-        f_score = conv_bn_layer(input=f_score, num_filters=1, filter_size=3, stride=1, name='f_score4')
+        f_score = conv_bn_layer(
+            input=f_common,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_score1')
+        f_score = conv_bn_layer(
+            input=f_score,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='f_score2')
+        f_score = conv_bn_layer(
+            input=f_score,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_score3')
+        f_score = conv_bn_layer(
+            input=f_score,
+            num_filters=1,
+            filter_size=3,
+            stride=1,
+            name='f_score4')
         f_score = fluid.layers.sigmoid(f_score)
         # print("f_score shape: {}".format(f_score.shape))
 
         #f_boder
-        f_border = conv_bn_layer(input=f_common, num_filters=64, filter_size=1, stride=1, act='relu', name='f_border1')
-        f_border = conv_bn_layer(input=f_border, num_filters=64, filter_size=3, stride=1, act='relu', name='f_border2')
-        f_border = conv_bn_layer(input=f_border, num_filters=128, filter_size=1, stride=1, act='relu', name='f_border3')
-        f_border = conv_bn_layer(input=f_border, num_filters=4, filter_size=3, stride=1, name='f_border4')
+        f_border = conv_bn_layer(
+            input=f_common,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_border1')
+        f_border = conv_bn_layer(
+            input=f_border,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='f_border2')
+        f_border = conv_bn_layer(
+            input=f_border,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_border3')
+        f_border = conv_bn_layer(
+            input=f_border,
+            num_filters=4,
+            filter_size=3,
+            stride=1,
+            name='f_border4')
         # print("f_border shape: {}".format(f_border.shape))
-        
+
         return f_score, f_border
 
     def SAST_Header2(self, f_common):
-        """Detector header.""" 
+        """Detector header."""
         #f_tvo
-        f_tvo = conv_bn_layer(input=f_common, num_filters=64, filter_size=1, stride=1, act='relu', name='f_tvo1')
-        f_tvo = conv_bn_layer(input=f_tvo, num_filters=64, filter_size=3, stride=1, act='relu', name='f_tvo2')
-        f_tvo = conv_bn_layer(input=f_tvo, num_filters=128, filter_size=1, stride=1, act='relu', name='f_tvo3')
-        f_tvo = conv_bn_layer(input=f_tvo, num_filters=8, filter_size=3, stride=1, name='f_tvo4')
+        f_tvo = conv_bn_layer(
+            input=f_common,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_tvo1')
+        f_tvo = conv_bn_layer(
+            input=f_tvo,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='f_tvo2')
+        f_tvo = conv_bn_layer(
+            input=f_tvo,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_tvo3')
+        f_tvo = conv_bn_layer(
+            input=f_tvo, num_filters=8, filter_size=3, stride=1, name='f_tvo4')
         # print("f_tvo shape: {}".format(f_tvo.shape))
 
         #f_tco
-        f_tco = conv_bn_layer(input=f_common, num_filters=64, filter_size=1, stride=1, act='relu', name='f_tco1')
-        f_tco = conv_bn_layer(input=f_tco, num_filters=64, filter_size=3, stride=1, act='relu', name='f_tco2')
-        f_tco = conv_bn_layer(input=f_tco, num_filters=128, filter_size=1, stride=1, act='relu', name='f_tco3')
-        f_tco = conv_bn_layer(input=f_tco, num_filters=2, filter_size=3, stride=1, name='f_tco4')
+        f_tco = conv_bn_layer(
+            input=f_common,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_tco1')
+        f_tco = conv_bn_layer(
+            input=f_tco,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name='f_tco2')
+        f_tco = conv_bn_layer(
+            input=f_tco,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_tco3')
+        f_tco = conv_bn_layer(
+            input=f_tco, num_filters=2, filter_size=3, stride=1, name='f_tco4')
         # print("f_tco shape: {}".format(f_tco.shape))
-        
+
         return f_tvo, f_tco
 
     def cross_attention(self, f_common):
         """
         """
         f_shape = fluid.layers.shape(f_common)
-        f_theta = conv_bn_layer(input=f_common, num_filters=128, filter_size=1, stride=1, act='relu', name='f_theta')
-        f_phi = conv_bn_layer(input=f_common, num_filters=128, filter_size=1, stride=1, act='relu', name='f_phi')
-        f_g = conv_bn_layer(input=f_common, num_filters=128, filter_size=1, stride=1, act='relu', name='f_g')
+        f_theta = conv_bn_layer(
+            input=f_common,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_theta')
+        f_phi = conv_bn_layer(
+            input=f_common,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_phi')
+        f_g = conv_bn_layer(
+            input=f_common,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_g')
         ### horizon
         fh_theta = f_theta
         fh_phi = f_phi
         fh_g = f_g
         #flatten
         fh_theta = fluid.layers.transpose(fh_theta, [0, 2, 3, 1])
-        fh_theta = fluid.layers.reshape(fh_theta, [f_shape[0] * f_shape[2], f_shape[3], 128])
+        fh_theta = fluid.layers.reshape(
+            fh_theta, [f_shape[0] * f_shape[2], f_shape[3], 128])
         fh_phi = fluid.layers.transpose(fh_phi, [0, 2, 3, 1])
-        fh_phi = fluid.layers.reshape(fh_phi, [f_shape[0] * f_shape[2], f_shape[3], 128])
+        fh_phi = fluid.layers.reshape(
+            fh_phi, [f_shape[0] * f_shape[2], f_shape[3], 128])
         fh_g = fluid.layers.transpose(fh_g, [0, 2, 3, 1])
-        fh_g = fluid.layers.reshape(fh_g, [f_shape[0] * f_shape[2], f_shape[3], 128])
+        fh_g = fluid.layers.reshape(fh_g,
+                                    [f_shape[0] * f_shape[2], f_shape[3], 128])
         #correlation
-        fh_attn = fluid.layers.matmul(fh_theta, fluid.layers.transpose(fh_phi, [0, 2, 1]))
+        fh_attn = fluid.layers.matmul(fh_theta,
+                                      fluid.layers.transpose(fh_phi, [0, 2, 1]))
         #scale
-        fh_attn = fh_attn / (128 ** 0.5)
+        fh_attn = fh_attn / (128**0.5)
         fh_attn = fluid.layers.softmax(fh_attn)
         #weighted sum
         fh_weight = fluid.layers.matmul(fh_attn, fh_g)
-        fh_weight = fluid.layers.reshape(fh_weight, [f_shape[0], f_shape[2], f_shape[3], 128])
+        fh_weight = fluid.layers.reshape(
+            fh_weight, [f_shape[0], f_shape[2], f_shape[3], 128])
         # print("fh_weight: {}".format(fh_weight.shape))
         fh_weight = fluid.layers.transpose(fh_weight, [0, 3, 1, 2])
-        fh_weight = conv_bn_layer(input=fh_weight, num_filters=128, filter_size=1, stride=1, name='fh_weight')
+        fh_weight = conv_bn_layer(
+            input=fh_weight,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            name='fh_weight')
         #short cut
-        fh_sc = conv_bn_layer(input=f_common, num_filters=128, filter_size=1, stride=1, name='fh_sc')
+        fh_sc = conv_bn_layer(
+            input=f_common,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            name='fh_sc')
         f_h = fluid.layers.relu(fh_weight + fh_sc)
         ######
         #vertical
@@ -174,31 +355,60 @@ class SASTHead(object):
         fv_g = fluid.layers.transpose(f_g, [0, 1, 3, 2])
         #flatten
         fv_theta = fluid.layers.transpose(fv_theta, [0, 2, 3, 1])
-        fv_theta = fluid.layers.reshape(fv_theta, [f_shape[0] * f_shape[3], f_shape[2], 128])
+        fv_theta = fluid.layers.reshape(
+            fv_theta, [f_shape[0] * f_shape[3], f_shape[2], 128])
         fv_phi = fluid.layers.transpose(fv_phi, [0, 2, 3, 1])
-        fv_phi = fluid.layers.reshape(fv_phi, [f_shape[0] * f_shape[3], f_shape[2], 128])
+        fv_phi = fluid.layers.reshape(
+            fv_phi, [f_shape[0] * f_shape[3], f_shape[2], 128])
         fv_g = fluid.layers.transpose(fv_g, [0, 2, 3, 1])
-        fv_g = fluid.layers.reshape(fv_g, [f_shape[0] * f_shape[3], f_shape[2], 128])
+        fv_g = fluid.layers.reshape(fv_g,
+                                    [f_shape[0] * f_shape[3], f_shape[2], 128])
         #correlation
-        fv_attn = fluid.layers.matmul(fv_theta, fluid.layers.transpose(fv_phi, [0, 2, 1]))
+        fv_attn = fluid.layers.matmul(fv_theta,
+                                      fluid.layers.transpose(fv_phi, [0, 2, 1]))
         #scale
-        fv_attn = fv_attn / (128 ** 0.5)
+        fv_attn = fv_attn / (128**0.5)
         fv_attn = fluid.layers.softmax(fv_attn)
         #weighted sum
         fv_weight = fluid.layers.matmul(fv_attn, fv_g)
-        fv_weight = fluid.layers.reshape(fv_weight, [f_shape[0], f_shape[3], f_shape[2], 128])
+        fv_weight = fluid.layers.reshape(
+            fv_weight, [f_shape[0], f_shape[3], f_shape[2], 128])
         # print("fv_weight: {}".format(fv_weight.shape))
         fv_weight = fluid.layers.transpose(fv_weight, [0, 3, 2, 1])
-        fv_weight = conv_bn_layer(input=fv_weight, num_filters=128, filter_size=1, stride=1, name='fv_weight')
+        fv_weight = conv_bn_layer(
+            input=fv_weight,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            name='fv_weight')
         #short cut
-        fv_sc = conv_bn_layer(input=f_common, num_filters=128, filter_size=1, stride=1, name='fv_sc')
+        fv_sc = conv_bn_layer(
+            input=f_common,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            name='fv_sc')
         f_v = fluid.layers.relu(fv_weight + fv_sc)
         ######
         f_attn = fluid.layers.concat([f_h, f_v], axis=1)
-        f_attn = conv_bn_layer(input=f_attn, num_filters=128, filter_size=1, stride=1, act='relu', name='f_attn')  
+        f_attn = conv_bn_layer(
+            input=f_attn,
+            num_filters=128,
+            filter_size=1,
+            stride=1,
+            act='relu',
+            name='f_attn')
         return f_attn
-        
+
     def __call__(self, blocks, with_cab=False):
+        """
+        Fuse different levels of feature map from backbone and predict results
+        Args:
+            blocks(list): feature maps from backbone
+            with_cab(bool): whether use cross_attention
+        Return: predicts
+        """
+
         # for k, v in blocks.items():
         #     print(k, v.shape)
 
@@ -212,12 +422,12 @@ class SASTHead(object):
         f_common = fluid.layers.elementwise_add(x=f_down, y=f_up)
         f_common = fluid.layers.relu(f_common)
         # print("f_common: {}".format(f_common.shape))
-        
+
         if self.with_cab:
             # print('enhence f_common with CAB.')
             f_common = self.cross_attention(f_common)
-            
-        f_score, f_border= self.SAST_Header1(f_common)
+
+        f_score, f_border = self.SAST_Header1(f_common)
         f_tvo, f_tco = self.SAST_Header2(f_common)
 
         predicts = OrderedDict()
@@ -225,4 +435,4 @@ class SASTHead(object):
         predicts['f_border'] = f_border
         predicts['f_tvo'] = f_tvo
         predicts['f_tco'] = f_tco
-        return predicts
+        return predicts
\ No newline at end of file

ppocr/modeling/heads/rec_srn_all_head.py

@@ -28,6 +28,13 @@ gradient_clip = 10
 
 
 class SRNPredict(object):
+    """
+    SRN:
+        see arxiv: https://arxiv.org/abs/2003.12294
+    args:
+        params(dict): the super parameters for network build
+    """
+
     def __init__(self, params):
         super(SRNPredict, self).__init__()
         self.char_num = params['char_num']
@@ -39,7 +46,15 @@ class SRNPredict(object):
         self.hidden_dims = params['hidden_dims']
 
     def pvam(self, inputs, others):
+        """
+        Parallel visual attention module model
+
+        args:
+            inputs(variable):  Feature map extracted from backbone network
+            others(list):  Other location information variables
 
+        return: pvam_features
+        """
         b, c, h, w = inputs.shape
         conv_features = fluid.layers.reshape(x=inputs, shape=[-1, c, h * w])
         conv_features = fluid.layers.transpose(x=conv_features, perm=[0, 2, 1])
@@ -98,6 +113,15 @@ class SRNPredict(object):
         return pvam_features
 
     def gsrm(self, pvam_features, others):
+        """
+        Global Semantic Reasonging Module
+
+        args:
+            pvam_features(variable):  Feature map extracted from pvam
+            others(list):  Other location information variables
+
+        return: gsrm_features, word_out, gsrm_out
+        """
 
         #===== GSRM Visual-to-semantic embedding block =====
         b, t, c = pvam_features.shape
@@ -190,7 +214,15 @@ class SRNPredict(object):
         return gsrm_features, word_out, gsrm_out
 
     def vsfd(self, pvam_features, gsrm_features):
+        """
+        Visual-Semantic Fusion Decoder Module
 
+        args:
+            pvam_features(variable):  Feature map extracted from pvam
+            gsrm_features(list):  Feature map extracted from gsrm
+
+        return: fc_out
+        """
         #===== Visual-Semantic Fusion Decoder Module =====
         b, t, c1 = pvam_features.shape
         b, t, c2 = gsrm_features.shape

ppocr/modeling/stns/tps.py

@@ -70,6 +70,13 @@ class LocalizationNetwork(object):
         return initial_bias
 
     def __call__(self, image):
+        """
+        Estimating parameters of geometric transformation
+        Args:
+            image: input
+        Return: 
+            batch_C_prime: the matrix of the geometric transformation
+        """
         F = self.F
         loc_lr = self.loc_lr
         if self.model_name == "large":
@@ -215,6 +222,14 @@ class GridGenerator(object):
         return batch_C_ex_part_tensor
 
     def __call__(self, batch_C_prime, I_r_size):
+        """
+        Generate the grid for the grid_sampler.
+        Args:
+            batch_C_prime: the matrix of the geometric transformation
+            I_r_size: the shape of the input image
+        Return: 
+            batch_P_prime: the grid for the grid_sampler 
+        """
         C = self.build_C()
         P = self.build_P(I_r_size)
         inv_delta_C = self.build_inv_delta_C(C).astype('float32')

ppocr/optimizer.py

@@ -29,9 +29,17 @@ def cosine_decay_with_warmup(learning_rate,
                              step_each_epoch,
                              epochs=500,
                              warmup_minibatch=1000):
-    """Applies cosine decay to the learning rate.
+    """
+    Applies cosine decay to the learning rate.
     lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
     decrease lr for every mini-batch and start with warmup.
+    args:
+        learning_rate(float): initial learning rate
+        step_each_epoch (int): number of step for each epoch in training process
+        epochs(int): number of training epochs
+        warmup_minibatch(int): number of minibatch for warmup
+    return:
+        lr(tensor): learning rate tensor
     """
     global_step = _decay_step_counter()
     lr = fluid.layers.tensor.create_global_var(
@@ -65,6 +73,7 @@ def AdamDecay(params, parameter_list=None):
         params(dict): the super parameters
         parameter_list (list): list of Variable names to update to minimize loss
     return:
+        optimizer: a Adam optimizer instance
     """
     base_lr = params['base_lr']
     beta1 = params['beta1']
@@ -121,6 +130,7 @@ def RMSProp(params, parameter_list=None):
         params(dict): the super parameters
         parameter_list (list): list of Variable names to update to minimize loss
     return:
+        optimizer: a RMSProp optimizer instance
     """
     base_lr = params.get("base_lr", 0.001)
     l2_decay = params.get("l2_decay", 0.00005)

ppocr/postprocess/db_postprocess.py

@@ -24,6 +24,7 @@ import string
 import cv2
 from shapely.geometry import Polygon
 import pyclipper
+from copy import deepcopy
 
 
 class DBPostProcess(object):
@@ -39,13 +40,15 @@ class DBPostProcess(object):
         self.min_size = 3
         self.dilation_kernel = np.array([[1, 1], [1, 1]])
 
-    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
-        '''
-        _bitmap: single map with shape (1, H, W),
-                whose values are binarized as {0, 1}
-        '''
-
-        bitmap = _bitmap
+    def boxes_from_bitmap(self, pred, mask):
+        """
+        Get boxes from the binarized image predicted by DB.
+        :param pred: the binarized image predicted by DB.
+        :param mask: new 'pred' after threshold filtering.
+        :return: (boxes, the score of each boxes)
+        """
+        dest_height, dest_width = pred.shape[-2:]
+        bitmap = deepcopy(mask)
         height, width = bitmap.shape
 
         outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
@@ -87,6 +90,11 @@ class DBPostProcess(object):
         return boxes, scores
 
     def unclip(self, box):
+        """
+        Shrink or expand the boxaccording to 'unclip_ratio'
+        :param box: The predicted box.
+        :return: uncliped box
+        """
         unclip_ratio = self.unclip_ratio
         poly = Polygon(box)
         distance = poly.area * unclip_ratio / poly.length
@@ -96,6 +104,11 @@ class DBPostProcess(object):
         return expanded
 
     def get_mini_boxes(self, contour):
+        """
+        Get boxes from the contour or box.
+        :param contour: The predicted contour.
+        :return: The predicted box.
+        """
         bounding_box = cv2.minAreaRect(contour)
         points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
 
@@ -119,6 +132,12 @@ class DBPostProcess(object):
         return box, min(bounding_box[1])
 
     def box_score_fast(self, bitmap, _box):
+        """
+        Calculate the score of box.
+        :param bitmap: The binarized image predicted by DB.
+        :param _box: The predicted box
+        :return: score
+        """
         h, w = bitmap.shape[:2]
         box = _box.copy()
         xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
@@ -137,13 +156,14 @@ class DBPostProcess(object):
 
         pred = pred[:, 0, :, :]
         segmentation = pred > self.thresh
-
         boxes_batch = []
         for batch_index in range(pred.shape[0]):
-            height, width = pred.shape[-2:]
 
-            mask = cv2.dilate(np.array(segmentation[batch_index]).astype(np.uint8), self.dilation_kernel)
-            tmp_boxes, tmp_scores = self.boxes_from_bitmap(pred[batch_index], mask, width, height)
+            mask = cv2.dilate(
+                np.array(segmentation[batch_index]).astype(np.uint8),
+                self.dilation_kernel)
+            tmp_boxes, tmp_scores = self.boxes_from_bitmap(pred[batch_index],
+                                                           mask)
 
             boxes = []
             for k in range(len(tmp_boxes)):

ppocr/utils/corpus/readme.md

+# Waiting for your contribution
+
+PaddleOCR welcomes you to provide multilingual corpus for us to synthesize more data to optimize the model.
+
+If you are interested, you can submit the corpus text to this directory and name it with {language}_corpus.txt.
+PaddleOCR thanks for your contribution.
\ No newline at end of file

ppocr/utils/corpus/readme_ch.md

+# 欢迎贡献语料
+
+PaddleOCR非常欢迎你提供多语言的语料，以供我们合成更多数据来优化模型。
+
+如你感兴趣，可将语料文本提交到此目录，并以 {语言}_corpus.txt 命名，PaddleOCR团队感谢你的贡献。
+
+
+

ppocr/utils/dict/occitan_dict.txt

+!
+"
+%
+&
+'
+(
+)
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+?
+[
+]
+«
+³
+µ
+º
+»
+A
+Á
+À
+B
+C
+Ç
+D
+E
+É
+È
+F
+G
+H
+I
+Í
+Ï
+J
+K
+L
+M
+N
+O
+Ó
+Ò
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+a
+á
+à
+b
+c
+d
+e
+é
+è
+f
+g
+h
+i
+í
+ï
+j
+k
+l
+m
+n
+o
+ó
+ò
+p
+q
+r
+s
+t
+u
+ú
+ü
+v
+w
+x
+y
+z
+ç
+æ
+Æ
+ê
+Ê
+ë
+Ë
+ñ
+Ñ
+ô
+Ô
+œ
+Œ
+ù
+Ù

ppocr/utils/utility.py

@@ -90,3 +90,15 @@ def check_and_read_gif(img_path):
         return imgvalue, True
     return None, False
 
+
+def create_multi_devices_program(program, loss_var_name):
+    build_strategy = fluid.BuildStrategy()
+    build_strategy.memory_optimize = False
+    build_strategy.enable_inplace = True
+    exec_strategy = fluid.ExecutionStrategy()
+    exec_strategy.num_iteration_per_drop_scope = 1
+    compile_program = fluid.CompiledProgram(program).with_data_parallel(
+        loss_name=loss_var_name,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy)
+    return compile_program

requirments.txt

@@ -3,4 +3,5 @@ imgaug
 pyclipper
 lmdb
 tqdm
-numpy
+numpy
\ No newline at end of file
+opencv-python

tools/infer/predict_rec.py

@@ -63,8 +63,9 @@ class TextRecognizer(object):
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         assert imgC == img.shape[2]
+        wh_ratio = max(max_wh_ratio, imgW * 1.0 / imgH)
         if self.character_type == "ch":
-            imgW = int((32 * max_wh_ratio))
+            imgW = int((32 * wh_ratio))
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW:

tools/infer/utility.py

@@ -84,19 +84,29 @@ def parse_args():
 
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
     parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
-    
+
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
-    
+
     return parser.parse_args()
 
 
 def create_predictor(args, mode):
+    """
+    create predictor for inference
+    :param args: params for prediction engine
+    :param mode: mode
+    :return: predictor
+    """
     if mode == "det":
         model_dir = args.det_model_dir
     elif mode == 'cls':
         model_dir = args.cls_model_dir
-    else:
+    elif mode == 'rec':
         model_dir = args.rec_model_dir
+    else:
+        raise ValueError(
+            "'mode' of create_predictor() can only be one of ['det', 'cls', 'rec']"
+        )
 
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
@@ -144,6 +154,12 @@ def create_predictor(args, mode):
 
 
 def draw_text_det_res(dt_boxes, img_path):
+    """
+    Visualize the results of detection
+    :param dt_boxes: The boxes predicted by detection model
+    :param img_path: Image path
+    :return: Visualized image
+    """
     src_im = cv2.imread(img_path)
     for box in dt_boxes:
         box = np.array(box).astype(np.int32).reshape(-1, 2)

tools/program.py

@@ -90,13 +90,13 @@ def load_config(file_path):
     merge_config(default_config)
     _, ext = os.path.splitext(file_path)
     assert ext in ['.yml', '.yaml'], "only support yaml files for now"
-    merge_config(yaml.load(open(file_path), Loader=yaml.Loader))
+    merge_config(yaml.load(open(file_path, 'rb'), Loader=yaml.Loader))
     assert "reader_yml" in global_config['Global'],\
         "absence reader_yml in global"
     reader_file_path = global_config['Global']['reader_yml']
     _, ext = os.path.splitext(reader_file_path)
     assert ext in ['.yml', '.yaml'], "only support yaml files for reader"
-    merge_config(yaml.load(open(reader_file_path), Loader=yaml.Loader))
+    merge_config(yaml.load(open(reader_file_path, 'rb'), Loader=yaml.Loader))
     return global_config