Merge pull request #1372 from tink2123/dygraph_doc

update reademe for dygraph

Merge pull request #1372 from tink2123/dygraph_doc
update reademe for dygraph
31b8aa54 · MissPenguin · GitHub · ba956b48 · 631fd9fd · 31b8aa54
Unverified Commit 31b8aa54 authored Dec 11, 2020 by MissPenguin Committed by GitHub Dec 11, 2020
4 changed files
--- a/README_ch.md
+++ b/README_ch.md
@@ -4,16 +4,18 @@
 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**
+- 2020.12.07 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
+- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
 - 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)
 ## 特性
 - PPOCR系列高质量预训练模型，准确的识别效果
@@ -48,13 +50,13 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - 代码体验：从[快速安装](./doc/doc_ch/installation.md) 开始
 <a name="模型下载"></a>
-## PP-OCR 1.1系列模型列表（9月17日更新）
+## PP-OCR 2.0系列模型列表（更新中）
 | 模型简介     | 模型名称     |推荐场景          | 检测模型 | 方向分类器 | 识别模型 |
 | ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar)      |
+| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](link) / [预训练模型](link)|[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link)      |
-| 中英文通用OCR模型（155.1M）   |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)          |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar)  |  
+| 中英文通用OCR模型（155.1M）   |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](link) / [预训练模型](link)    |[推理模型](link) / [预训练模型](link)    |[推理模型](link) / [预训练模型](link)  |  
-| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)| [推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|  
+| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](link) / [slim模型](link) |[推理模型](link) / [slim模型](link)| [推理模型](link) / [slim模型](link)|  
 更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](./doc/doc_ch/models_list.md)
@@ -141,6 +143,7 @@ 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
 ## 贡献代码
 我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
 - 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
 - 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
 - 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
@@ -148,3 +151,6 @@ 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。
+- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了PPOCRLabel的完整代码。
--- a/README_en.md
+++ b/README_en.md
-English | [简体中文](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**
- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
+- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README_en.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.8.16, Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
+- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
+- 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)
- 2020.7.15, Add mobile App demo , support both iOS and  Android  ( based on easyedge and Paddle Lite)
+- 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)
- 2020.7.15, Improve the  deployment ability, add the C + +  inference , serving deployment. In addition, the benchmarks of the ultra-lightweight OCR model are provided.
+- 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.
- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
+- 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)
 ## Features
- Ultra-lightweight OCR model, total model size is only 8.6M
+- PPOCR series of high-quality pre-trained models, comparable to commercial effects
-    - Single model supports Chinese/English numbers combination recognition, vertical text recognition, long text recognition
+    - Ultra lightweight ppocr_mobile series models: detection (2.6M) + direction classifier (0.9M) + recognition (4.6M) = 8.1M
-    - Detection model DB (4.1M) + recognition model CRNN (4.5M)
+    - General ppocr_server series models: detection (47.2M) + direction classifier (0.9M) + recognition (107M) = 155.1M
- Various text detection algorithms: EAST, DB
+    - Ultra lightweight compression ppocr_mobile_slim series models: detection (1.4M) + direction classifier (0.5M) + recognition (1.6M) = 3.5M
- Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
+- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
- Support Linux, Windows, macOS and other systems.
+- Support multi-language recognition: Korean, Japanese, German, French
+- Support user-defined training, provides rich predictive inference deployment solutions
+- Support PIP installation, easy to use
+- Support Linux, Windows, MacOS and other systems
 ## Visualization
-![](doc/imgs_results/11.jpg)
+<div align="center">
+    <img src="doc/imgs_results/1101.jpg" width="800">
+    <img src="doc/imgs_results/1103.jpg" width="800">
+</div>
-![](doc/imgs_results/img_10.jpg)
+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).
-[More visualization](./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)
@@ -42,177 +58,110 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
 <a name="Supported-Chinese-model-list"></a>
-### Supported Models:
+## PP-OCR 2.0 series model list（Update on Sep 17）
-|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
+| Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
-|-|-|-|-|-|
+| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-|db_crnn_mobile|ultra-lightweight OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
+| Chinese and English ultra-lightweight OCR model (8.1M)       | ch_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) |  
-|db_crnn_server|General OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
+| Chinese and English general OCR model (155.1M)               | ch_ppocr_server_v1.1_xx      | Server            | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) |  
+| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile            | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) |    [inference model](link) / [slim model](link) |
+| French ultra-lightweight OCR model (4.6M)       | french_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - | [inference model](link) / [pre-trained model](link)  |
+| German ultra-lightweight OCR model (4.6M)       | german_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) |
+| Korean ultra-lightweight OCR model (5.9M)       | korean_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)|
+| Japan ultra-lightweight OCR model (6.2M)       | japan_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)  |
+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)
 - [Quick Start](./doc/doc_en/quickstart_en.md)
- Algorithm introduction
+- [Code Structure](./doc/doc_en/tree_en.md)
-    - [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
+- Algorithm Introduction
-    - [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
+    - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
-    - [END-TO-END OCR Algorithm](#ENDENDOCRALGORITHM)
+    - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
- Model training/evaluation
+    - [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)
+    - [Direction Classification](./doc/doc_en/angle_class_en.md)
    - [Yml Configuration](./doc/doc_en/config_en.md)
-    - [Tricks](./doc/doc_en/tricks_en.md)
+- Inference and Deployment
- Deployment
+    - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
    - [Python Inference](./doc/doc_en/inference_en.md)
    - [C++ Inference](./deploy/cpp_infer/readme_en.md)
-    - [Serving](./doc/doc_en/serving_en.md)
+    - [Serving](./deploy/hubserving/readme_en.md)
    - [Mobile](./deploy/lite/readme_en.md)
-    - Model Quantization and Compression (coming soon)
+    - [Model Quantization](./deploy/slim/quantization/README_en.md)
-    - [Benchmark](./doc/doc_en/benchmark_en.md)
+    - [Model Compression](./deploy/slim/prune/README_en.md)
+    - [Benchmark](./doc/doc_en/benchmark_en.md)  
+- Data Annotation and Synthesis
+    - [Semi-automatic Annotation Tool](./PPOCRLabel/README_en.md)
+    - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
+    - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
 - Datasets
    - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
    - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
    - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
-    - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
+- [Visualization](#Visualization)
-    - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
+- [New language requests](#language_requests)
- [FAQ](#FAQ)
+- [FAQ](./doc/doc_en/FAQ_en.md)
- Visualization
-    - [Ultra-lightweight Chinese/English OCR Visualization](#UCOCRVIS)
-    - [General Chinese/English OCR Visualization](#GeOCRVIS)
-    - [Chinese/English OCR Visualization (Support Space Recognition )](#SpaceOCRVIS)
 - [Community](#Community)
 - [References](./doc/doc_en/reference_en.md)
 - [License](#LICENSE)
 - [Contribution](#CONTRIBUTION)
-<a name="TEXTDETECTIONALGORITHM"></a>
+<a name="PP-OCR-Pipeline"></a>
-## Text Detection Algorithm
-PaddleOCR open source text detection algorithms list:
- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))(Baidu Self-Research)
-On the ICDAR2015 dataset, the text detection result is as follows:
-|Model|Backbone|precision|recall|Hmean|Download link|
-|-|-|-|-|-|-|
-|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
-|EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
-|DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
-|DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
-|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
-On Total-Text dataset, the text detection result is as follows:
-|Model|Backbone|precision|recall|Hmean|Download link|
-|-|-|-|-|-|-|
-|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
-**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
-For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for text detection task are as follows:  
-|Model|Backbone|Configuration file|Pre-trained model|
-|-|-|-|-|
-|ultra-lightweight OCR model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
-|General OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
-* Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
-For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
+## PP-OCR Pipeline
-<a name="TEXTRECOGNITIONALGORITHM"></a>
+<div align="center">
-## Text Recognition Algorithm
+    <img src="./doc/ppocr_framework.png" width="800">
+</div>
-PaddleOCR open-source text recognition algorithms list:
- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research)
-Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
-|Model|Backbone|Avg Accuracy|Module combination|Download link|
-|-|-|-|-|-|
-|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
-|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
-|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
-|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
-|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
-|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
-|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
-|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
-|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[Download link](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
-**Note：** SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from [Baidu Drive](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA) (download code: y3ry).
-The average accuracy of the two-stage training in the original paper is 89.74%, and that of one stage training in paddleocr is 88.33%. Both pre-trained weights can be downloaded [here](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar).
-We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w  training data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the model. The related configuration and pre-trained models are as follows:
-|Model|Backbone|Configuration file|Pre-trained model|
-|-|-|-|-|
-|ultra-lightweight OCR model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)|
-|General OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
-Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
-<a name="ENDENDOCRALGORITHM"></a>
+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).
-## END-TO-END OCR Algorithm
- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, coming soon)
-## Visualization
-<a name="UCOCRVIS"></a>
-### 1.Ultra-lightweight Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
+## Visualization [more](./doc/doc_en/visualization_en.md)
+- Chinese OCR model
 <div align="center">
-    <img src="doc/imgs_results/1.jpg" width="800">
+    <img src="./doc/imgs_results/1102.jpg" width="800">
+    <img src="./doc/imgs_results/1104.jpg" width="800">
+    <img src="./doc/imgs_results/1106.jpg" width="800">
+    <img src="./doc/imgs_results/1105.jpg" width="800">
 </div>
-<a name="GeOCRVIS"></a>
+- English OCR model
-### 2. General Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
 <div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
+    <img src="./doc/imgs_results/img_12.jpg" width="800">
 </div>
-<a name="SpaceOCRVIS"></a>
+- Multilingual OCR model
-### 3.Chinese/English OCR Visualization (Space_support) [more](./doc/doc_en/visualization_en.md)
 <div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
+    <img src="./doc/imgs_results/1110.jpg" width="800">
+    <img src="./doc/imgs_results/1112.jpg" width="800">
 </div>
-<a name="FAQ"></a>
-## FAQ
-1. Error when using attention-based recognition model: KeyError: 'predict'
-    The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
-2. About inference speed
-    When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
+<a name="language_requests"></a>
+## Guideline for new language requests
-3. Service deployment and mobile deployment
+If you want to request a new language support, a PR with 2 following files are needed：
-    It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
+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.
-4. Release time of self-developed algorithm
+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.
-    Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
+If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.
-[more](./doc/doc_en/FAQ_en.md)
+More details, please refer to [Multilingual OCR Development Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
-<a name="Community"></a>
-## Community
-Scan  the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
-<div align="center">
-<img src="./doc/joinus.jpg"  width = "200" height = "200" />
-</div>
 <a name="LICENSE"></a>
 ## License
@@ -229,3 +178,7 @@ 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.
+- Thanks [LKKlein](https://github.com/LKKlein) for contributing a new deploying package with the Golang program language.
+- Thanks [Evezerest](https://github.com/Evezerest), [ninetailskim](https://github.com/ninetailskim), [edencfc](https://github.com/edencfc), [BeyondYourself](https://github.com/BeyondYourself) and [1084667371](https://github.com/1084667371) for contributing a new data annotation tool, i.e., PPOCRLabel。
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -17,17 +17,17 @@ PaddleOCR开源的文本检测算法列表：
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 |-|-|-|-|-|-|
-|EAST|ResNet50_vd||||[敬请期待]()|
+|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](link)|
-|EAST|MobileNetV3||||[敬请期待]()|
+|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](link)|
-|DB|ResNet50_vd||||[敬请期待]()|
+|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](link)|
-|DB|MobileNetV3||||[敬请期待]()|
+|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](link)|
-|SAST|ResNet50_vd||||[敬请期待]()|
+|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](link))|
 在Total-text文本检测公开数据集上，算法效果如下：
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 |-|-|-|-|-|-|
-|SAST|ResNet50_vd||||[敬请期待]()|
+|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](link)|
 **说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
@@ -37,23 +37,23 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
 <a name="文本识别算法"></a>
 ### 2.文本识别算法
-PaddleOCR开源的文本识别算法列表：
+PaddleOCR基于动态图开源的文本识别算法列表：
 - [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐）
 - [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
 - [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
+- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294)) coming soon
 参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
 |-|-|-|-|-|
-|Rosetta|Resnet34_vd||rec_r34_vd_none_none_ctc|[敬请期待]()|
+|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](link)|
-|Rosetta|MobileNetV3||rec_mv3_none_none_ctc|[敬请期待]()|
+|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](link)|
-|CRNN|Resnet34_vd||rec_r34_vd_none_bilstm_ctc|[敬请期待]()|
+|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](link)|
-|CRNN|MobileNetV3||rec_mv3_none_bilstm_ctc|[敬请期待]()|
+|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](link)|
-|STAR-Net|Resnet34_vd||rec_r34_vd_tps_bilstm_ctc|[敬请期待]()|
+|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](link)|
-|STAR-Net|MobileNetV3||rec_mv3_tps_bilstm_ctc|[敬请期待]()|
+|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](link)|
 PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -19,17 +19,17 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |Model|Backbone|precision|recall|Hmean|Download link|
 |-|-|-|-|-|-|
-|EAST|ResNet50_vd||||[Coming soon]()|
+|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](link)|
-|EAST|MobileNetV3||||[Coming soon]()|
+|EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](link)|
-|DB|ResNet50_vd||||[Coming soon]()|
+|DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](link)|
-|DB|MobileNetV3||||[Coming soon]()|
+|DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](link)|
-|SAST|ResNet50_vd||||[Coming soon]()|
+|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[Download link](link)|
 On Total-Text dataset, the text detection result is as follows:
 |Model|Backbone|precision|recall|Hmean|Download link|
 |-|-|-|-|-|-|
-|SAST|ResNet50_vd||||[Coming soon]()|
+|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[Download link](link)|
 **Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
@@ -42,19 +42,19 @@ PaddleOCR open-source text recognition algorithms list:
 - [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
 - [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
 - [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research)
+- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research) coming soon
 Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
 |Model|Backbone|Avg Accuracy|Module combination|Download link|
 |-|-|-|-|-|
-|Rosetta|Resnet34_vd||rec_r34_vd_none_none_ctc|[Coming soon]()|
+|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
-|Rosetta|MobileNetV3||rec_mv3_none_none_ctc|[Coming soon]()|
+|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
-|CRNN|Resnet34_vd||rec_r34_vd_none_bilstm_ctc|[Coming soon]()|
+|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
-|CRNN|MobileNetV3||rec_mv3_none_bilstm_ctc|[Coming soon]()|
+|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
-|STAR-Net|Resnet34_vd||rec_r34_vd_tps_bilstm_ctc|[Coming soon]()|
+|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
-|STAR-Net|MobileNetV3||rec_mv3_tps_bilstm_ctc|[Coming soon]()|
+|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
 Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)