init commit

PPOCRLabel/resources/icons/save-as.svg

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+     id="metadata590">
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PPOCRLabel/resources/strings/strings-en.properties

+openFile=Open
+openFileDetail=Open image or label file
+quit=Quit
+quitApp=Quit application
+openDir=Open Dir
+openDatasetDir=Open DatasetDir
+copyPrevBounding=Copy previous Bounding Boxes in the current image 
+changeSavedAnnotationDir=Change default saved Annotation dir
+openAnnotation=Open Annotation
+openAnnotationDetail=Open an annotation file
+changeSaveDir=Change Save Dir
+nextImg=Next Image
+nextImgDetail=Open the next Image
+prevImg=Prev Image
+prevImgDetail=Open the previous Image
+verifyImg=Verify Image
+verifyImgDetail=Verify Image
+save=Check
+saveDetail=Save the labels to a file
+changeSaveFormat=Change save format
+saveAs=Save As
+saveAsDetail=Save the labels to a different file
+closeCur=Close
+closeCurDetail=Close the current file
+deleteImg=Delete current image
+deleteImgDetail=Delete the current image
+resetAll=Reset Interface and Save Dir
+resetAllDetail=Reset All
+boxLineColor=Box Line Color
+boxLineColorDetail=Choose Box line color
+crtBox=Create RectBox
+crtBoxDetail=Draw a new box
+delBox=Delete RectBox
+delBoxDetail=Remove the box
+dupBox=Duplicate RectBox
+dupBoxDetail=Create a duplicate of the selected box
+tutorial=PaddleOCR url
+tutorialDetail=Show demo
+info=Information
+zoomin=Zoom In
+zoominDetail=Increase zoom level
+zoomout=Zoom Out
+zoomoutDetail=Decrease zoom level
+originalsize=Original size
+originalsizeDetail=Zoom to original size
+fitWin=Fit Window
+fitWinDetail=Zoom follows window size
+fitWidth=Fit Width
+fitWidthDetail=Zoom follows window width
+editLabel=Edit Label
+editLabelDetail=Modify the label of the selected Box
+shapeLineColor=Shape Line Color
+shapeLineColorDetail=Change the line color for this specific shape
+shapeFillColor=Shape Fill Color
+shapeFillColorDetail=Change the fill color for this specific shape
+showHide=Show/Hide Label Panel
+useDefaultLabel=Use default label
+useDifficult=Difficult
+boxLabelText=Box Labels
+labels=Labels
+autoSaveMode=Auto Save mode
+singleClsMode=Single Class Mode
+displayLabel=Display Labels
+fileList=File List
+files=Files
+advancedMode=Advanced Mode
+advancedModeDetail=Swtich to advanced mode
+showAllBoxDetail=Show all bounding boxes
+hideAllBoxDetail=Hide all bounding boxes
+annoPanel=anno Panel
+anno=anno
+addNewBbox=new bbox
+reLabel=reLabel
+choosemodel=Choose OCR model
+tipchoosemodel=Choose OCR model from dir
+ImageResize=Image Resize
+IR=Image Resize
+autoRecognition=Auto Recognition
+reRecognition=Re-recognition
+mfile=File
+medit=Edit
+mview=View
+mhelp=Help
+iconList=Icon List
+detectionBoxposition=Detection box position
+recognitionResult=Recognition result
+creatPolygon=Create PolygonBox
+rotateLeft=Left turn 90 degrees
+rotateRight=Right turn 90 degrees
+drawSquares=Draw Squares
+saveRec=Export Recognition Result
+tempLabel=TEMPORARY
+nullLabel=NULL
+steps=Steps
+keys=Shortcut Keys
+choseModelLg=Choose Model Language
+cancel=Cancel
+ok=OK
+autolabeling=Automatic Labeling
+hideBox=Hide All Box
+showBox=Show All Box
+saveLabel=Export Label
+singleRe=Re-recognition RectBox
+labelDialogOption=Pop-up Label Input Dialog
+undo=Undo
+undoLastPoint=Undo Last Point
+autoSaveMode=Auto Export Label Mode
+lockBox=Lock selected box/Unlock all box
+lockBoxDetail=Lock selected box/Unlock all box
+keyListTitle=Key List
+keyDialogTip=Enter object label
+keyChange=Change Box Key
+TableRecognition=Table Recognition
+cellreRecognition=Cell Re-Recognition
+exportJSON=Export Excel Label(PubTabNet)

PPOCRLabel/resources/strings/strings-zh-CN.properties

+saveAsDetail=將标签保存到其他文件
+changeSaveDir=改变存放目录
+openFile=打开文件
+shapeLineColorDetail=更改线条颜色
+resetAll=重置界面与保存地址
+crtBox=矩形标注
+crtBoxDetail=创建一个新的区块
+dupBoxDetail=复制区块
+verifyImg=验证图像
+zoominDetail=放大
+verifyImgDetail=验证图像
+saveDetail=保存标签文件
+openFileDetail=打开图像文件
+fitWidthDetail=调整宽度适应到窗口宽度
+tutorial=PaddleOCR地址
+editLabel=编辑标签
+openAnnotationDetail=打开标签文件
+quit=退出
+shapeFillColorDetail=更改填充颜色
+closeCurDetail=关闭当前文件
+closeCur=关闭文件
+deleteImg=删除图像
+deleteImgDetail=删除当前图像
+fitWin=调整到窗口大小
+delBox=删除选择的区块
+boxLineColorDetail=选择线框颜色
+originalsize=原始大小
+resetAllDetail=重置所有设定
+zoomoutDetail=放大画面
+save=确认
+saveAs=另存为
+fitWinDetail=缩放到当前窗口大小
+openDir=打开目录
+openDatasetDir=打开数据集路径
+copyPrevBounding=复制当前图像中的上一个边界框
+showHide=显示/隐藏标签
+changeSaveFormat=更改存储格式
+shapeFillColor=填充颜色
+quitApp=退出程序
+dupBox=复制区块
+delBoxDetail=删除区块
+zoomin=放大画面
+info=信息
+openAnnotation=开启标签
+prevImgDetail=上一个图像
+fitWidth=缩放到当前画面宽度
+zoomout=缩小画面
+changeSavedAnnotationDir=更改保存标签文件的预设目录
+nextImgDetail=下一个图像
+originalsizeDetail=放大到原始大小
+prevImg=上一张
+tutorialDetail=显示示范内容
+shapeLineColor=形状线条颜色
+boxLineColor=区块线条颜色
+editLabelDetail=修改当前所选的区块颜色
+nextImg=下一张
+useDefaultLabel=使用预设标签
+useDifficult=有难度的
+boxLabelText=区块的标签
+labels=标签
+autoSaveMode=自动保存模式
+singleClsMode=单一类别模式
+displayLabel=显示类别
+fileList=文件列表
+files=文件
+advancedMode=专家模式
+advancedModeDetail=切换到专家模式
+showAllBoxDetail=显示所有区块
+hideAllBoxDetail=隐藏所有区块
+annoPanel=标注面板
+anno=标注
+addNewBbox=新框
+reLabel=重标注
+choosemodel=选择模型
+tipchoosemodel=选择OCR模型
+ImageResize=图片缩放
+IR=图片缩放
+autoRecognition=自动标注
+reRecognition=重新识别
+mfile=文件
+medit=编辑
+mview=视图
+mhelp=帮助
+iconList=缩略图
+detectionBoxposition=检测框位置
+recognitionResult=识别结果
+creatPolygon=多点标注
+drawSquares=正方形标注
+rotateLeft=图片左旋转90度
+rotateRight=图片右旋转90度
+saveRec=导出识别结果
+tempLabel=待识别
+nullLabel=无法识别
+steps=操作步骤
+keys=快捷键
+choseModelLg=选择模型语言
+cancel=取消
+ok=确认
+autolabeling=自动标注中
+hideBox=隐藏所有标注
+showBox=显示所有标注
+saveLabel=导出标记结果
+singleRe=重识别此区块
+labelDialogOption=弹出标记输入框
+undo=撤销
+undoLastPoint=撤销上个点
+autoSaveMode=自动导出标记结果
+lockBox=锁定框/解除锁定框
+lockBoxDetail=若当前没有框处于锁定状态则锁定选中的框，若存在锁定框则解除所有锁定框的锁定状态
+keyListTitle=关键词列表
+keyDialogTip=请输入类型名称
+keyChange=更改Box关键字类别
+TableRecognition=表格识别
+cellreRecognition=单元格重识别
+exportJSON=导出表格JSON标注
\ No newline at end of file

PPOCRLabel/setup.cfg

+[bumpversion]
+commit = True
+tag = True
+
+[bumpversion:file:setup.py]
+
+[bdist_wheel]
+universal = 1

PPOCRLabel/setup.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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
+#
+#     http://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.
+
+from setuptools import setup
+from io import open
+
+with open('requirements.txt', encoding="utf-8-sig") as f:
+    requirements = f.readlines()
+    requirements.append('tqdm')
+
+
+def readme():
+    with open('README.md', encoding="utf-8-sig") as f:
+        README = f.read()
+    return README
+
+
+setup(
+    name='PPOCRLabel',
+    packages=['PPOCRLabel'],
+    package_data = {'PPOCRLabel': ['libs/*','resources/strings/*','resources/icons/*']},
+    package_dir={'PPOCRLabel': ''},
+    include_package_data=True,
+    entry_points={"console_scripts": ["PPOCRLabel= PPOCRLabel.PPOCRLabel:main"]},
+    version='1.0.2',
+    install_requires=requirements,
+    license='Apache License 2.0',
+    description='PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PPOCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models',
+    long_description=readme(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/PaddlePaddle/PaddleOCR',
+    download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
+    keywords=[
+        'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
+    ],
+    classifiers=[
+        'Intended Audience :: Developers', 'Operating System :: OS Independent',
+        'Natural Language :: English',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
+    ], )
\ No newline at end of file

README.md

+English | [简体中文](README_ch.md)
+
+<p align="center">
+ <img src="./doc/PaddleOCR_log.png" align="middle" width = "600"/>
+<p align="center">
+<p align="left">
+    <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
+    <a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
+    <a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
+    <a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
+</p>
+
+## Introduction
+
+PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
+
+<div align="center">
+    <img src="./doc/imgs_results/PP-OCRv3/en/en_4.png" width="800">
+</div>
+
+<div align="center">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00006737.jpg" width="800">
+</div>
+
+## Recent updates
+- **🔥2022.7 Release [OCR scene application collection](./applications/README_en.md)**
+    - PaddleOCR scene application covers general, manufacturing, finance, transportation industry of the main OCR vertical applications, including digital tube, LCD screen character, license plate, high-precision SVTR model, etc. **7 vertical models**.
+- **🔥2022.5.9 Release PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
+    - Release [PP-OCRv3](./doc/doc_en/ppocr_introduction_en.md#pp-ocrv3): With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%.
+    - Release [PPOCRLabelv2](./PPOCRLabel): Add the annotation function for table recognition task, key information extraction task and irregular text image.
+    - Release interactive e-book [*"Dive into OCR"*](./doc/doc_en/ocr_book_en.md), covers the cutting-edge theory and code practice of OCR full stack technology.
+- 2021.12.21 Release PaddleOCR [release/2.4](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4)
+    - Release 1 text detection algorithm ([PSENet](./doc/doc_en/algorithm_det_psenet_en.md)), 3 text recognition algorithms ([NRTR](./doc/doc_en/algorithm_rec_nrtr_en.md)、[SEED](./doc/doc_en/algorithm_rec_seed_en.md)、[SAR](./doc/doc_en/algorithm_rec_nrtr_en.md)).
+    - Release 1 key information extraction algorithm [SDMGR](./ppstructure/docs/kie_en.md) and 3 [DocVQA](./ppstructure/vqa) algorithms (LayoutLM, LayoutLMv2, LayoutXLM).
+- 2021.9.7 Release PaddleOCR [release/2.3](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.3)
+    - Release [PP-OCRv2](./doc/doc_en/ppocr_introduction_en.md#pp-ocrv2). The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
+- 2021.8.3 Release PaddleOCR [release/2.2](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.2)
+    - Release a new structured documents analysis toolkit, i.e., [PP-Structure](./ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
+- [more](./doc/doc_en/update_en.md)
+
+
+## Features
+
+PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution [PP-OCR](./doc/doc_en/ppocr_introduction_en.md) and [PP-Structure](./ppstructure/README.md) on this basis, and get through the whole process of data production, model training, compression, inference and deployment.
+
+![](./doc/features_en.png)
+
+> It is recommended to start with the “quick start” in the document tutorial
+
+
+## Quick Experience
+
+- One line of code quick use: [Quick Start](./doc/doc_en/quickstart_en.md)
+- Web online experience for the ultra-lightweight OCR: [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
+- Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for  installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
+
+
+
+<a name="book"></a>
+## E-book: *Dive Into OCR*
+- [Dive Into OCR 📚](./doc/doc_en/ocr_book_en.md)
+
+
+<a name="Community"></a>
+## Community👬
+
+- For international developers, we regard [PaddleOCR Discussions](https://github.com/PaddlePaddle/PaddleOCR/discussions) as our international community platform. All ideas and questions can be discussed here in English.
+
+- For Chinese develops, Scan the QR code below with your Wechat, you can join the official technical discussion group. For richer community content, please refer to [中文README](README_ch.md), looking forward to your participation.
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG"  width = "150" height = "150" />
+</div>
+
+<a name="Supported-Chinese-model-list"></a>
+
+## PP-OCR Series Model List（Update on September 8th）
+
+| Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
+| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Chinese and English ultra-lightweight PP-OCRv3 model（16.2M）     | ch_PP-OCRv3_xx          | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
+| English ultra-lightweight PP-OCRv3 model（13.4M）     | en_PP-OCRv3_xx          | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
+| Chinese and English ultra-lightweight PP-OCRv2 model（11.6M） |  ch_PP-OCRv2_xx |Mobile & Server|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
+| Chinese and English ultra-lightweight PP-OCR model (9.4M)       | ch_ppocr_mobile_v2.0_xx      | Mobile & server   |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
+| Chinese and English general PP-OCR model (143.4M)               | ch_ppocr_server_v2.0_xx      | Server            |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
+
+
+- For more model downloads (including multiple languages), please refer to [PP-OCR 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).
+- For structural document analysis models, please refer to [PP-Structure models](./ppstructure/docs/models_list_en.md).
+
+## Tutorials
+- [Environment Preparation](./doc/doc_en/environment_en.md)
+- [PP-OCR 🔥](./doc/doc_en/ppocr_introduction_en.md)
+    - [Quick Start](./doc/doc_en/quickstart_en.md)
+    - [Model Zoo](./doc/doc_en/models_en.md)
+    - [Model training](./doc/doc_en/training_en.md)
+        - [Text Detection](./doc/doc_en/detection_en.md)
+        - [Text Recognition](./doc/doc_en/recognition_en.md)
+        - [Text Direction Classification](./doc/doc_en/angle_class_en.md)
+    - Model Compression
+        - [Model Quantization](./deploy/slim/quantization/README_en.md)
+        - [Model Pruning](./deploy/slim/prune/README_en.md)
+        - [Knowledge Distillation](./doc/doc_en/knowledge_distillation_en.md)
+    - [Inference and Deployment](./deploy/README.md)
+        - [Python Inference](./doc/doc_en/inference_ppocr_en.md)
+        - [C++ Inference](./deploy/cpp_infer/readme.md)
+        - [Serving](./deploy/pdserving/README.md)
+        - [Mobile](./deploy/lite/readme.md)
+        - [Paddle2ONNX](./deploy/paddle2onnx/readme.md)
+        - [PaddleCloud](./deploy/paddlecloud/README.md)
+        - [Benchmark](./doc/doc_en/benchmark_en.md)  
+- [PP-Structure 🔥](./ppstructure/README.md)
+    - [Quick Start](./ppstructure/docs/quickstart_en.md)
+    - [Model Zoo](./ppstructure/docs/models_list_en.md)
+    - [Model training](./doc/doc_en/training_en.md)  
+        - [Layout Parser](./ppstructure/layout/README.md)
+        - [Table Recognition](./ppstructure/table/README.md)
+        - [DocVQA](./ppstructure/vqa/README.md)
+        - [Key Information Extraction](./ppstructure/docs/kie_en.md)
+    - [Inference and Deployment](./deploy/README.md)
+        - [Python Inference](./ppstructure/docs/inference_en.md)
+        - [C++ Inference]()
+        - [Serving](./deploy/pdserving/README.md)
+- [Academic algorithms](./doc/doc_en/algorithms_en.md)
+    - [Text detection](./doc/doc_en/algorithm_overview_en.md)
+    - [Text recognition](./doc/doc_en/algorithm_overview_en.md)
+    - [End-to-end](./doc/doc_en/algorithm_overview_en.md)
+    - [Add New Algorithms to PaddleOCR](./doc/doc_en/add_new_algorithm_en.md)
+- Data Annotation and Synthesis
+    - [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
+    - [Data Synthesis Tool: Style-Text](./StyleText/README.md)
+    - [Other Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
+    - [Other Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
+- Datasets
+    - [General OCR Datasets(Chinese/English)](doc/doc_en/dataset/datasets_en.md)
+    - [HandWritten_OCR_Datasets(Chinese)](doc/doc_en/dataset/handwritten_datasets_en.md)
+    - [Various OCR Datasets(multilingual)](doc/doc_en/dataset/vertical_and_multilingual_datasets_en.md)
+    - [layout analysis](doc/doc_en/dataset/layout_datasets_en.md)
+    - [table recognition](doc/doc_en/dataset/table_datasets_en.md)
+    - [DocVQA](doc/doc_en/dataset/docvqa_datasets_en.md)
+- [Code Structure](./doc/doc_en/tree_en.md)
+- [Visualization](#Visualization)
+- [Community](#Community)
+- [New language requests](#language_requests)
+- [FAQ](./doc/doc_en/FAQ_en.md)
+- [References](./doc/doc_en/reference_en.md)
+- [License](#LICENSE)
+
+
+<a name="Visualization"></a>
+## Visualization [more](./doc/doc_en/visualization_en.md)
+
+<details open>
+<summary>PP-OCRv3 Chinese model</summary>
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic001.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic002.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic003.jpg" width="800">
+</div>
+</details>
+
+<details open>
+<summary>PP-OCRv3 English model</summary>
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/en/en_1.png" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/en/en_2.png" width="800">
+</div>
+</details>
+
+<details open>
+<summary>PP-OCRv3 Multilingual model</summary>
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/multi_lang/japan_2.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/multi_lang/korean_1.jpg" width="800">
+</div>
+</details>
+
+<details open>
+<summary>PP-Structure</summary>
+
+- layout analysis + table recognition  
+<div align="center">
+    <img src="./ppstructure/docs/table/ppstructure.GIF" width="800">
+</div>
+
+- SER (Semantic entity recognition)
+<div align="center">
+    <img src="./ppstructure/docs/vqa/result_ser/zh_val_0_ser.jpg" width="800">
+</div>
+
+- RE (Relation Extraction)
+<div align="center">
+    <img src="./ppstructure/docs/vqa/result_re/zh_val_21_re.jpg" width="800">
+</div>
+
+</details>
+
+<a name="language_requests"></a>
+## Guideline for New Language Requests
+
+If you want to request a new language support, a PR with 1 following files are needed：
+
+1. In folder [ppocr/utils/dict](./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.
+
+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
+This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>

README_ch.md

+[English](README.md) | 简体中文
+
+<p align="center">
+ <img src="./doc/PaddleOCR_log.png" align="middle" width = "600"/>
+<p align="center">
+<p align="left">
+    <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
+    <a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
+    <a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
+    <a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
+</p>
+
+## 简介
+
+PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力开发者训练出更好的模型，并应用落地。
+
+<div align="center">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00006737.jpg" width="800">
+</div>
+
+## 近期更新
+
+- **🔥2022.7 发布[OCR场景应用集合](./applications)**
+  - 发布OCR场景应用集合，包含数码管、液晶屏、车牌、高精度SVTR模型等**7个垂类模型**，覆盖通用，制造、金融、交通行业的主要OCR垂类应用。
+  
+- **🔥2022.5.9 发布PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
+    - 发布[PP-OCRv3](./doc/doc_ch/ppocr_introduction.md#pp-ocrv3)，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上；
+    - 发布半自动标注工具[PPOCRLabelv2](./PPOCRLabel)：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能；
+    - 发布OCR产业落地工具集：打通22种训练部署软硬件环境与方式，覆盖企业90%的训练部署环境需求；
+    - 发布交互式OCR开源电子书[《动手学OCR》](./doc/doc_ch/ocr_book.md)，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。
+- 2021.12.21 发布PaddleOCR [release/2.4](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4)
+    - OCR算法新增1种文本检测算法（[PSENet](./doc/doc_ch/algorithm_det_psenet.md)），3种文本识别算法（[NRTR](./doc/doc_ch/algorithm_rec_nrtr.md)、[SEED](./doc/doc_ch/algorithm_rec_seed.md)、[SAR](./doc/doc_ch/algorithm_rec_sar.md)）；
+    - 文档结构化算法新增1种关键信息提取算法（[SDMGR](./ppstructure/docs/kie.md)），3种[DocVQA](./ppstructure/vqa)算法（LayoutLM、LayoutLMv2，LayoutXLM）。
+- 2021.9.7 发布PaddleOCR [release/2.3](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.3)
+    - 发布[PP-OCRv2](./doc/doc_ch/ppocr_introduction.md#pp-ocrv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
+- 2021.8.3 发布PaddleOCR [release/2.2](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.2)
+    - 发布文档结构分析[PP-Structure](./ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
+
+> [更多](./doc/doc_ch/update.md)
+
+## 特性
+
+支持多种OCR相关前沿算法，在此基础上打造产业级特色模型[PP-OCR](./doc/doc_ch/ppocr_introduction.md)和[PP-Structure](./ppstructure/README_ch.md)，并打通数据生产、模型训练、压缩、预测部署全流程。
+
+![](./doc/features.png)
+
+> 上述内容的使用方法建议从文档教程中的快速开始体验
+
+
+## 快速开始
+
+- 在线网站体验：超轻量PP-OCR mobile模型体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
+- 移动端demo体验：[安装包DEMO下载地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)
+- 一行命令快速使用：[快速开始（中英文/多语言/文档分析）](./doc/doc_ch/quickstart.md)
+
+<a name="电子书"></a>
+## 《动手学OCR》电子书
+- [《动手学OCR》电子书📚](./doc/doc_ch/ocr_book.md)
+
+
+<a name="开源社区"></a>
+## 开源社区
+- **项目合作📑：** 如果您是企业开发者且有明确的OCR垂类应用需求，填写[问卷](https://paddle.wjx.cn/vj/QwF7GKw.aspx)后可免费与官方团队展开不同层次的合作。
+- **加入社区👬：** 微信扫描二维码并填写问卷之后，加入交流群领取福利
+  - **获取PaddleOCR最新发版解说《OCR超强技术详解与产业应用实战》系列直播课回放链接**
+  - **10G重磅OCR学习大礼包：**《动手学OCR》电子书，配套讲解视频和notebook项目；66篇OCR相关顶会前沿论文打包放送，包括CVPR、AAAI、IJCAI、ICCV等；PaddleOCR历次发版直播课视频；OCR社区优秀开发者项目分享视频。
+- **社区项目**🏅️：[社区项目](./doc/doc_ch/thirdparty.md)文档中包含了社区用户**使用PaddleOCR开发的各种工具、应用**以及**为PaddleOCR贡献的功能、优化的文档与代码**等，是官方为社区开发者打造的荣誉墙，也是帮助优质项目宣传的广播站。
+- **社区常规赛**🎁：社区常规赛是面向OCR开发者的积分赛事，覆盖文档、代码、模型和应用四大类型，以季度为单位评选并发放奖励，赛题详情与报名方法可参考[链接](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG"  width = "150" height = "150" />
+</div>
+
+
+
+<a name="模型下载"></a>
+## PP-OCR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PP-OCRv3模型（16.2M）     | ch_PP-OCRv3_xx          | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
+| 英文超轻量PP-OCRv3模型（13.4M）     | en_PP-OCRv3_xx          | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
+
+- 超轻量OCR系列更多模型下载（包括多语言），可以参考[PP-OCR系列模型下载](./doc/doc_ch/models_list.md)，文档分析相关模型参考[PP-Structure系列模型下载](./ppstructure/docs/models_list.md)
+
+### PaddleOCR场景应用模型
+
+| 行业 | 类别         | 亮点                               | 文档说明                                                     | 模型下载                                      |
+| ---- | ------------ | ---------------------------------- | ------------------------------------------------------------ | --------------------------------------------- |
+| 制造 | 数码管识别   | 数码管数据合成、漏识别调优         | [光功率计数码管字符识别](./applications/光功率计数码管字符识别/光功率计数码管字符识别.md) | [下载链接](./applications/README.md#模型下载) |
+| 金融 | 通用表单识别 | 多模态通用表单结构化提取           | [多模态表单识别](./applications/多模态表单识别.md)           | [下载链接](./applications/README.md#模型下载) |
+| 交通 | 车牌识别     | 多角度图像处理、轻量模型、端侧部署 | [轻量级车牌识别](./applications/轻量级车牌识别.md)           | [下载链接](./applications/README.md#模型下载) |
+
+- 更多制造、金融、交通行业的主要OCR垂类应用模型（如电表、液晶屏、高精度SVTR模型等），可参考[场景应用模型下载](./applications)
+
+<a name="文档教程"></a>
+
+## 文档教程
+
+- [运行环境准备](./doc/doc_ch/environment.md)
+- [PP-OCR文本检测识别🔥](./doc/doc_ch/ppocr_introduction.md)
+    - [快速开始](./doc/doc_ch/quickstart.md)
+    - [模型库](./doc/doc_ch/models_list.md)
+    - [模型训练](./doc/doc_ch/training.md)
+        - [文本检测](./doc/doc_ch/detection.md)
+        - [文本识别](./doc/doc_ch/recognition.md)
+        - [文本方向分类器](./doc/doc_ch/angle_class.md)
+    - 模型压缩
+        - [模型量化](./deploy/slim/quantization/README.md)
+        - [模型裁剪](./deploy/slim/prune/README.md)
+        - [知识蒸馏](./doc/doc_ch/knowledge_distillation.md)
+    - [推理部署](./deploy/README_ch.md)
+        - [基于Python预测引擎推理](./doc/doc_ch/inference_ppocr.md)
+        - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
+        - [服务化部署](./deploy/pdserving/README_CN.md)
+        - [端侧部署](./deploy/lite/readme.md)
+        - [Paddle2ONNX模型转化与预测](./deploy/paddle2onnx/readme.md)
+        - [云上飞桨部署工具](./deploy/paddlecloud/README.md)
+        - [Benchmark](./doc/doc_ch/benchmark.md)
+- [PP-Structure文档分析🔥](./ppstructure/README_ch.md)
+    - [快速开始](./ppstructure/docs/quickstart.md)
+    - [模型库](./ppstructure/docs/models_list.md)
+    - [模型训练](./doc/doc_ch/training.md)
+        - [版面分析](./ppstructure/layout/README_ch.md)
+        - [表格识别](./ppstructure/table/README_ch.md)
+        - [关键信息提取](./ppstructure/docs/kie.md)
+        - [DocVQA](./ppstructure/vqa/README_ch.md)
+    - [推理部署](./deploy/README_ch.md)
+        - [基于Python预测引擎推理](./ppstructure/docs/inference.md)
+        - [基于C++预测引擎推理]()
+        - [服务化部署](./deploy/pdserving/README_CN.md)
+- [前沿算法与模型🚀](./doc/doc_ch/algorithm.md)
+    - [文本检测算法](./doc/doc_ch/algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95)
+    - [文本识别算法](./doc/doc_ch/algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
+    - [端到端算法](./doc/doc_ch/algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
+    - [使用PaddleOCR架构添加新算法](./doc/doc_ch/add_new_algorithm.md)
+- [场景应用](./applications)
+- 数据标注与合成
+    - [半自动标注工具PPOCRLabel](./PPOCRLabel/README_ch.md)
+    - [数据合成工具Style-Text](./StyleText/README_ch.md)
+    - [其它数据标注工具](./doc/doc_ch/data_annotation.md)
+    - [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
+- 数据集
+    - [通用中英文OCR数据集](doc/doc_ch/dataset/datasets.md)
+    - [手写中文OCR数据集](doc/doc_ch/dataset/handwritten_datasets.md)
+    - [垂类多语言OCR数据集](doc/doc_ch/dataset/vertical_and_multilingual_datasets.md)
+    - [版面分析数据集](doc/doc_ch/dataset/layout_datasets.md)
+    - [表格识别数据集](doc/doc_ch/dataset/table_datasets.md)
+    - [DocVQA数据集](doc/doc_ch/dataset/docvqa_datasets.md)
+- [代码组织结构](./doc/doc_ch/tree.md)
+- [效果展示](#效果展示)
+- [《动手学OCR》电子书📚](./doc/doc_ch/ocr_book.md)
+- [开源社区](#开源社区)
+- FAQ
+    - [通用问题](./doc/doc_ch/FAQ.md)
+    - [PaddleOCR实战问题](./doc/doc_ch/FAQ.md)
+- [参考文献](./doc/doc_ch/reference.md)
+- [许可证书](#许可证书)
+
+
+<a name="效果展示"></a>
+
+## 效果展示 [more](./doc/doc_ch/visualization.md)
+
+<details open>
+<summary>PP-OCRv3 中文模型</summary>
+
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic001.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic002.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/ch/PP-OCRv3-pic003.jpg" width="800">
+</div>
+
+</details>
+
+
+<details open>
+<summary>PP-OCRv3 英文模型</summary>
+
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/en/en_1.png" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/en/en_2.png" width="800">
+</div>
+
+</details>
+
+
+<details open>
+<summary>PP-OCRv3 多语言模型</summary>
+
+<div align="center">
+    <img src="doc/imgs_results/PP-OCRv3/multi_lang/japan_2.jpg" width="800">
+    <img src="doc/imgs_results/PP-OCRv3/multi_lang/korean_1.jpg" width="800">
+</div>
+
+</details>
+
+<details open>
+<summary>PP-Structure 文档分析</summary>
+
+- 版面分析+表格识别  
+<div align="center">
+    <img src="./ppstructure/docs/table/ppstructure.GIF" width="800">
+</div>
+
+- SER（语义实体识别）  
+<div align="center">
+    <img src="./ppstructure/docs/vqa/result_ser/zh_val_0_ser.jpg" width="800">
+</div>
+
+- RE（关系提取）
+<div align="center">
+    <img src="./ppstructure/docs/vqa/result_re/zh_val_21_re.jpg" width="800">
+</div>
+
+</details>
+
+<a name="许可证书"></a>
+
+## 许可证书
+本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。

StyleText/README.md

+English | [简体中文](README_ch.md)
+
+## Style Text
+
+### Contents
+- [1. Introduction](#Introduction)
+- [2. Preparation](#Preparation)
+- [3. Quick Start](#Quick_Start)
+- [4. Applications](#Applications)
+- [5. Code Structure](#Code_structure)
+
+
+<a name="Introduction"></a>
+### Introduction
+
+<div align="center">
+    <img src="doc/images/3.png" width="800">
+</div>
+
+<div align="center">
+    <img src="doc/images/9.png" width="600">
+</div>
+
+
+The Style-Text data synthesis tool is a tool based on Baidu and HUST cooperation research work, "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
+
+Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
+* (1) Text foreground style transfer module.
+* (2) Background extraction module.
+* (3) Fusion module.
+
+After these three steps, you can quickly realize the image text style transfer. The following figure is some results of the data synthesis tool.
+
+<div align="center">
+    <img src="doc/images/10.png" width="1000">
+</div>
+
+
+<a name="Preparation"></a>
+#### Preparation
+
+1. Please refer the [QUICK INSTALLATION](../doc/doc_en/installation_en.md) to install PaddlePaddle. Python3 environment is strongly recommended.
+2. Download the pretrained models and unzip:
+
+```bash
+cd StyleText
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip
+unzip style_text_models.zip
+```
+
+If you save the model in another location, please modify the address of the model file in `configs/config.yml`, and you need to modify these three configurations at the same time:
+
+```
+bg_generator:
+  pretrain: style_text_models/bg_generator
+...
+text_generator:
+  pretrain: style_text_models/text_generator
+...
+fusion_generator:
+  pretrain: style_text_models/fusion_generator
+```
+
+<a name="Quick_Start"></a>
+### Quick Start
+
+#### Synthesis single image
+
+1. You can run `tools/synth_image` and generate the demo image, which is saved in the current folder.
+
+```python
+python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
+```
+
+* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko).
+* Note 2: Synth-Text is mainly used to generate images for OCR recognition models.
+  So the height of style images should be around 32 pixels. Images in other sizes may behave poorly.
+* Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction.
+
+
+
+For example, enter the following image and corpus `PaddleOCR`.
+
+<div align="center">
+    <img src="examples/style_images/2.jpg" width="300">
+</div>
+
+The result `fake_fusion.jpg` will be generated.
+
+<div align="center">
+    <img src="doc/images/4.jpg" width="300">
+</div>
+
+What's more, the medium result `fake_bg.jpg` will also be saved, which is the background output.
+
+<div align="center">
+    <img src="doc/images/7.jpg" width="300">
+</div>
+
+
+`fake_text.jpg` * `fake_text.jpg` is the generated image with the same font style as `Style Input`.
+
+
+<div align="center">
+    <img src="doc/images/8.jpg" width="300">
+</div>
+
+
+#### Batch synthesis
+
+In actual application scenarios, it is often necessary to synthesize pictures in batches and add them to the training set. StyleText can use a batch of style pictures and corpus to synthesize data in batches. The synthesis process is as follows:
+
+1. The referenced dataset can be specifed in `configs/dataset_config.yml`:
+
+   * `Global`：
+     * `output_dir:`：Output synthesis data path.
+   * `StyleSampler`：
+     * `image_home`：style images' folder.
+     * `label_file`：Style images' file list. If label is provided, then it is the label file path.
+     * `with_label`：Whether the `label_file` is label file list.
+   * `CorpusGenerator`：
+     * `method`：Method of CorpusGenerator，supports `FileCorpus` and `EnNumCorpus`. If `EnNumCorpus` is used，No other configuration is needed，otherwise you need to set `corpus_file` and `language`.
+     * `language`：Language of the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko). 
+     * `corpus_file`: Filepath of the corpus. Corpus file should be a text file which will be split by line-endings（'\n'）. Corpus generator samples one line each time.
+
+
+Example of corpus file:
+```
+PaddleOCR
+飞桨文字识别
+StyleText
+风格文本图像数据合成
+```
+
+We provide a general dataset containing Chinese, English and Korean (50,000 images in all) for your trial ([download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/chkoen_5w.tar)), some examples are given below :
+
+<div align="center">
+     <img src="doc/images/5.png" width="800">
+</div>
+
+2. You can run the following command to start synthesis task:
+
+   ``` bash
+   python3 tools/synth_dataset.py -c configs/dataset_config.yml
+   ```
+
+We also provide example corpus and images in `examples` folder.
+    <div align="center">
+        <img src="examples/style_images/1.jpg" width="300">
+        <img src="examples/style_images/2.jpg" width="300">
+    </div>
+If you run the code above directly, you will get example output data in `output_data` folder.
+You will get synthesis images and labels as below:
+   <div align="center">
+       <img src="doc/images/12.png" width="800">
+   </div>
+There will be some cache under the `label` folder. If the program exit unexpectedly, you can find cached labels there.
+When the program finish normally, you will find all the labels in `label.txt` which give the final results.
+
+<a name="Applications"></a>
+### Applications
+We take two scenes as examples, which are metal surface English number recognition and general Korean recognition, to illustrate practical cases of using StyleText to synthesize data to improve text recognition. The following figure shows some examples of real scene images and composite images:
+
+<div align="center">
+    <img src="doc/images/11.png" width="800">
+</div>
+
+
+After adding the above synthetic data for training, the accuracy of the recognition model is improved, which is shown in the following table:
+
+
+| Scenario | Characters | Raw Data | Test Data | Only Use Raw Data</br>Recognition Accuracy | New Synthetic Data | Simultaneous Use of Synthetic Data</br>Recognition Accuracy | Index Improvement |
+| -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
+| Metal surface | English and numbers | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
+| Random background | Korean       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
+
+
+<a name="Code_structure"></a>
+### Code Structure
+
+```
+StyleText
+|-- arch                        // Network module files.
+|   |-- base_module.py
+|   |-- decoder.py
+|   |-- encoder.py
+|   |-- spectral_norm.py
+|   `-- style_text_rec.py
+|-- configs                     // Config files.
+|   |-- config.yml
+|   `-- dataset_config.yml
+|-- engine                      // Synthesis engines.
+|   |-- corpus_generators.py    // Sample corpus from file or generate random corpus.
+|   |-- predictors.py           // Predict using network.
+|   |-- style_samplers.py       // Sample style images.
+|   |-- synthesisers.py         // Manage other engines to synthesis images.
+|   |-- text_drawers.py         // Generate standard input text images.
+|   `-- writers.py              // Write synthesis images and labels into files.
+|-- examples                    // Example files.
+|   |-- corpus
+|   |   `-- example.txt
+|   |-- image_list.txt
+|   `-- style_images
+|       |-- 1.jpg
+|       `-- 2.jpg
+|-- fonts                       // Font files.
+|   |-- ch_standard.ttf
+|   |-- en_standard.ttf
+|   `-- ko_standard.ttf
+|-- tools                       // Program entrance.
+|   |-- __init__.py
+|   |-- synth_dataset.py        // Synthesis dataset.
+|   `-- synth_image.py          // Synthesis image.
+`-- utils                       // Module of basic functions.
+    |-- config.py
+    |-- load_params.py
+    |-- logging.py
+    |-- math_functions.py
+    `-- sys_funcs.py
+```

StyleText/README_ch.md

+简体中文 | [English](README.md)
+
+## Style Text
+
+
+### 目录
+- [一、工具简介](#工具简介)
+- [二、环境配置](#环境配置)
+- [三、快速上手](#快速上手)
+- [四、应用案例](#应用案例)
+- [五、代码结构](#代码结构)
+
+<a name="工具简介"></a>
+### 一、工具简介
+<div align="center">
+    <img src="doc/images/3.png" width="800">
+</div>
+
+<div align="center">
+    <img src="doc/images/1.png" width="600">
+</div>
+
+
+Style-Text数据合成工具是基于百度和华科合作研发的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
+
+不同于常用的基于GAN的数据合成工具，Style-Text主要框架包括：1.文本前景风格迁移模块 2.背景抽取模块 3.融合模块。经过这样三步，就可以迅速实现图像文本风格迁移。下图是一些该数据合成工具效果图。
+
+<div align="center">
+    <img src="doc/images/2.png" width="1000">
+</div>
+
+<a name="环境配置"></a>
+### 二、环境配置
+
+1. 参考[快速安装](../doc/doc_ch/installation.md)，安装PaddleOCR。
+2. 进入`StyleText`目录，下载模型，并解压：
+
+```bash
+cd StyleText
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip
+unzip style_text_models.zip
+```
+
+如果您将模型保存再其他位置，请在`configs/config.yml`中修改模型文件的地址，修改时需要同时修改这三个配置：
+
+```
+bg_generator:
+  pretrain: style_text_models/bg_generator
+...
+text_generator:
+  pretrain: style_text_models/text_generator
+...
+fusion_generator:
+  pretrain: style_text_models/fusion_generator
+```
+
+<a name="快速上手"></a>
+### 三、快速上手
+
+#### 合成单张图
+输入一张风格图和一段文字语料，运行tools/synth_image，合成单张图片，结果图像保存在当前目录下：
+
+```python
+python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
+```
+* 注1：语言选项和语料相对应，目前支持英文(en)、简体中文(ch)和韩语(ko)。
+* 注2：Style-Text生成的数据主要应用于OCR识别场景。基于当前PaddleOCR识别模型的设计，我们主要支持高度在32左右的风格图像。
+  如果输入图像尺寸相差过多，效果可能不佳。
+* 注3：可以通过修改配置文件`configs/config.yml`中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。
+
+
+例如，输入如下图片和语料"PaddleOCR":
+
+<div align="center">
+    <img src="examples/style_images/2.jpg" width="300">
+</div>
+
+生成合成数据`fake_fusion.jpg`：
+<div align="center">
+    <img src="doc/images/4.jpg" width="300">
+</div>
+
+除此之外，程序还会生成并保存中间结果`fake_bg.jpg`：为风格参考图去掉文字后的背景；
+
+<div align="center">
+    <img src="doc/images/7.jpg" width="300">
+</div>
+
+`fake_text.jpg`：是用提供的字符串，仿照风格参考图中文字的风格，生成在灰色背景上的文字图片。
+
+<div align="center">
+    <img src="doc/images/8.jpg" width="300">
+</div>
+
+#### 批量合成
+在实际应用场景中，经常需要批量合成图片，补充到训练集中。Style-Text可以使用一批风格图片和语料，批量合成数据。合成过程如下：
+
+1. 在`configs/dataset_config.yml`中配置目标场景风格图像和语料的路径，具体如下：
+
+   * `Global`：
+     * `output_dir:`：保存合成数据的目录。
+   * `StyleSampler`：
+     * `image_home`：风格图片目录；
+     * `label_file`：风格图片路径列表文件，如果所用数据集有label，则label_file为label文件路径；
+     * `with_label`：标志`label_file`是否为label文件。
+   * `CorpusGenerator`：
+     * `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`；
+     * `language`：语料的语种，目前支持英文(en)、简体中文(ch)和韩语(ko)；
+     * `corpus_file`: 语料文件路径。语料文件应使用文本文件。语料生成器首先会将语料按行切分，之后每次随机选取一行。
+
+   语料文件格式示例：
+   ```
+   PaddleOCR
+   飞桨文字识别
+   StyleText
+   风格文本图像数据合成
+   ...
+   ```
+
+   Style-Text也提供了一批中英韩5万张通用场景数据用作文本风格图像，便于合成场景丰富的文本图像，下图给出了一些示例。
+
+   中英韩5万张通用场景数据: [下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/chkoen_5w.tar)
+
+<div align="center">
+    <img src="doc/images/5.png" width="800">
+</div>
+
+2. 运行`tools/synth_dataset`合成数据：
+
+   ``` bash
+   python3 tools/synth_dataset.py -c configs/dataset_config.yml
+   ```
+   我们在examples目录下提供了样例图片和语料。
+    <div align="center">
+        <img src="examples/style_images/1.jpg" width="300">
+        <img src="examples/style_images/2.jpg" width="300">
+    </div>
+
+   直接运行上述命令，可以在output_data中产生样例输出，包括图片和用于训练识别模型的标注文件：
+   <div align="center">
+       <img src="doc/images/12.png" width="800">
+   </div>
+
+   其中label目录下的标注文件为程序运行过程中产生的缓存，如果程序在中途异常终止，可以使用缓存的标注文件。
+   如果程序正常运行完毕，则会在output_data下生成label.txt，为最终的标注结果。
+
+<a name="应用案例"></a>
+### 四、应用案例
+下面以金属表面英文数字识别和通用韩语识别两个场景为例，说明使用Style-Text合成数据，来提升文本识别效果的实际案例。下图给出了一些真实场景图像和合成图像的示例：
+
+<div align="center">
+    <img src="doc/images/6.png" width="800">
+</div>
+
+在添加上述合成数据进行训练后，识别模型的效果提升，如下表所示：
+
+| 场景     | 字符       | 原始数据 | 测试数据 | 只使用原始数据</br>识别准确率 | 新增合成数据 | 同时使用合成数据</br>识别准确率 | 指标提升 |
+| -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
+| 金属表面 | 英文和数字 | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
+| 随机背景 | 韩语       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
+
+
+<a name="代码结构"></a>
+### 五、代码结构
+
+```
+StyleText
+|-- arch                        // 网络结构定义文件
+|   |-- base_module.py
+|   |-- decoder.py
+|   |-- encoder.py
+|   |-- spectral_norm.py
+|   `-- style_text_rec.py
+|-- configs                     // 配置文件
+|   |-- config.yml
+|   `-- dataset_config.yml
+|-- engine                      // 数据合成引擎
+|   |-- corpus_generators.py    // 从文本采样或随机生成语料
+|   |-- predictors.py           // 调用网络生成数据
+|   |-- style_samplers.py       // 采样风格图片
+|   |-- synthesisers.py         // 调度各个模块，合成数据
+|   |-- text_drawers.py         // 生成标准文字图片，用作输入
+|   `-- writers.py              // 将合成的图片和标签写入本地目录
+|-- examples                    // 示例文件
+|   |-- corpus
+|   |   `-- example.txt
+|   |-- image_list.txt
+|   `-- style_images
+|       |-- 1.jpg
+|       `-- 2.jpg
+|-- fonts                       // 字体文件
+|   |-- ch_standard.ttf
+|   |-- en_standard.ttf
+|   `-- ko_standard.ttf
+|-- tools                       // 程序入口
+|   |-- __init__.py
+|   |-- synth_dataset.py        // 批量合成数据
+|   `-- synth_image.py          // 合成单张图片
+`-- utils                       // 其他基础功能模块
+    |-- config.py
+    |-- load_params.py
+    |-- logging.py
+    |-- math_functions.py
+    `-- sys_funcs.py
+```