Return separate Excel sheets for tables while using paddleocr structure

.github/ISSUE_TEMPLATE/custom.md

@@ -13,3 +13,5 @@ assignees: ''
 - 版本号/Version：Paddle：  PaddleOCR： 问题相关组件/Related components：
 - 运行指令/Command Code：
 - 完整报错/Complete Error Message：
+
+请尽量不要包含图片在问题中/Please try to not include the image in the issue.

.github/ISSUE_TEMPLATE/newfeature.md

@@ -0,0 +1,17 @@
+---
+name: New Feature Issue template
+about: Issue template for new features.
+title: ''
+labels: 'Code PR is needed'
+assignees: 'shiyutang'
+
+---
+
+## 背景
+
+经过需求征集https://github.com/PaddlePaddle/PaddleOCR/issues/10334 和每周技术研讨会 https://github.com/PaddlePaddle/PaddleOCR/issues/10223 讨论，我们确定了XXXX任务。
+
+## 解决步骤
+1. 根据开源代码进行网络结构、评估指标转换。代码链接：XXXX
+2. 结合[论文复现指南](https://github.com/PaddlePaddle/models/blob/release%2F2.2/tutorials/article-implementation/ArticleReproduction_CV.md)，进行前反向对齐等操作，达到论文Table.1中的指标。
+3. 参考[PR提交规范](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/code_and_doc.md)提交代码PR到ppocr中。

.github/pull_request_template.md

@@ -0,0 +1,15 @@
+### PR 类型 PR types
+<!-- One of [ New features | Bug fixes | Function optimization | Performance optimization | Breaking changes | Others ] -->
+
+### PR 变化内容类型 PR changes
+<!-- One of [ Models | APIs | Docs | Others ] -->
+
+### 描述 Description
+<!-- Describe what this PR does -->
+
+### 提PR之前的检查 Check-list
+
+- [ ] 这个 PR 是提交到dygraph分支或者是一个cherry-pick，否则请先提交到dygarph分支。
+      This PR is pushed to the dygraph branch or cherry-picked from the dygraph branch. Otherwise, please push your changes to the dygraph branch.
+- [ ] 这个PR清楚描述了功能，帮助评审能提升效率。This PR have fully described what it does such that reviewers can speedup.
+- [ ] 这个PR已经经过本地测试。This PR can be covered by existing tests or locally verified. 

.github/workflows/python-publish.yml

@@ -0,0 +1,41 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+        pip install setuptools
+        pip install wheel
+    - name: Build package
+      run: python setup.py bdist_wheel
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

.pre-commit-config.yaml

@@ -1,10 +1,6 @@
--   repo: https://github.com/PaddlePaddle/mirrors-yapf.git
-    sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
-    hooks:
-    -   id: yapf
-        files: \.py$
+repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    sha: a11d9314b22d8f8c7556443875b731ef05965464
+    rev: a11d9314b22d8f8c7556443875b731ef05965464
     hooks:
     -   id: check-merge-conflict
     -   id: check-symlinks
@@ -15,7 +11,7 @@
     -   id: trailing-whitespace
         files: \.md$
 -   repo: https://github.com/Lucas-C/pre-commit-hooks
-    sha: v1.0.1
+    rev: v1.0.1
     hooks:
     -   id: forbid-crlf
         files: \.md$
@@ -33,3 +29,14 @@
         entry: bash .clang_format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
+# For Python files
+-   repo: https://github.com/psf/black.git
+    rev: 23.3.0
+    hooks:
+    -   id: black
+        files: (.*\.(py|pyi|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.2.0
+    hooks:
+    -   id: ruff
+        args: [--fix, --exit-non-zero-on-fix, --no-cache]

PPOCRLabel/gen_ocr_train_val_test.py

@@ -17,48 +17,43 @@ def isCreateOrDeleteFolder(path, flag):
     return flagAbsPath
 
 
-def splitTrainVal(root, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
-    # 按照指定的比例划分训练集、验证集、测试集
-    dataAbsPath = os.path.abspath(root)
-
-    if flag == "det":
-        labelFilePath = os.path.join(dataAbsPath, args.detLabelFileName)
-    elif flag == "rec":
-        labelFilePath = os.path.join(dataAbsPath, args.recLabelFileName)
-
-    labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
-    labelFileContent = labelFileRead.readlines()
-    random.shuffle(labelFileContent)
-    labelRecordLen = len(labelFileContent)
-
-    for index, labelRecordInfo in enumerate(labelFileContent):
-        imageRelativePath = labelRecordInfo.split('\t')[0]
-        imageLabel = labelRecordInfo.split('\t')[1]
-        imageName = os.path.basename(imageRelativePath)
-
-        if flag == "det":
-            imagePath = os.path.join(dataAbsPath, imageName)
-        elif flag == "rec":
-            imagePath = os.path.join(dataAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
-
-        # 按预设的比例划分训练集、验证集、测试集
-        trainValTestRatio = args.trainValTestRatio.split(":")
-        trainRatio = eval(trainValTestRatio[0]) / 10
-        valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
-        curRatio = index / labelRecordLen
-
-        if curRatio < trainRatio:
-            imageCopyPath = os.path.join(absTrainRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
-        elif curRatio >= trainRatio and curRatio < valRatio:
-            imageCopyPath = os.path.join(absValRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
-        else:
-            imageCopyPath = os.path.join(absTestRootPath, imageName)
-            shutil.copy(imagePath, imageCopyPath)
-            testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+def splitTrainVal(root, abs_train_root_path, abs_val_root_path, abs_test_root_path, train_txt, val_txt, test_txt, flag):
+
+    data_abs_path = os.path.abspath(root)
+    label_file_name = args.detLabelFileName if flag == "det" else args.recLabelFileName
+    label_file_path = os.path.join(data_abs_path, label_file_name)
+
+    with open(label_file_path, "r", encoding="UTF-8") as label_file:
+        label_file_content = label_file.readlines()
+        random.shuffle(label_file_content)
+        label_record_len = len(label_file_content)
+
+        for index, label_record_info in enumerate(label_file_content):
+            image_relative_path, image_label = label_record_info.split('\t')
+            image_name = os.path.basename(image_relative_path)
+
+            if flag == "det":
+                image_path = os.path.join(data_abs_path, image_name)
+            elif flag == "rec":
+                image_path = os.path.join(data_abs_path, args.recImageDirName, image_name)
+
+            train_val_test_ratio = args.trainValTestRatio.split(":")
+            train_ratio = eval(train_val_test_ratio[0]) / 10
+            val_ratio = train_ratio + eval(train_val_test_ratio[1]) / 10
+            cur_ratio = index / label_record_len
+
+            if cur_ratio < train_ratio:
+                image_copy_path = os.path.join(abs_train_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                train_txt.write("{}\t{}\n".format(image_copy_path, image_label))
+            elif cur_ratio >= train_ratio and cur_ratio < val_ratio:
+                image_copy_path = os.path.join(abs_val_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                val_txt.write("{}\t{}\n".format(image_copy_path, image_label))
+            else:
+                image_copy_path = os.path.join(abs_test_root_path, image_name)
+                shutil.copy(image_path, image_copy_path)
+                test_txt.write("{}\t{}\n".format(image_copy_path, image_label))
 
 
 # 删掉存在的文件
@@ -148,4 +143,4 @@ def genDetRecTrainVal(args):
         help="the name of the folder where the cropped recognition dataset is located"
     )
     args = parser.parse_args()
-    genDetRecTrainVal(args)
+    genDetRecTrainVal(args)

PPOCRLabel/libs/utils.py

@@ -209,10 +209,10 @@ def convert_token(html_list):
                 token_list.append("<td")
                 if 'colspan' in col:
                     _, n = col.split('colspan=')
-                    token_list.append(" colspan=\"{}\"".format(n[0]))
+                    token_list.append(" colspan=\"{}\"".format(str(int(n))))
                 if 'rowspan' in col:
                     _, n = col.split('rowspan=')
-                    token_list.append(" rowspan=\"{}\"".format(n[0]))
+                    token_list.append(" rowspan=\"{}\"".format(str(int(n))))
                 token_list.extend([">", "</td>"])
         token_list.append("</tr>")
     token_list.append("</tbody>")

README.md

@@ -26,6 +26,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 </div>
 
 ## 📣 近期更新
+- **🔥[PaddleOCR 算法模型挑战赛](https://competition.atomgit.com/competitionInfo?id=d25e62a0d7f27876a8c4219bfc0be90e)** 火热开启！报名时间1/15-3/31，30万元奖金池！快来一展身手吧😎！
+- **🔨2023.11 发布 [PP-ChatOCRv2](https://aistudio.baidu.com/application/detail/10368)**: 一个SDK，覆盖20+高频应用场景，支持5种文本图像智能分析能力和部署，包括通用场景关键信息抽取（快递单、营业执照和机动车行驶证等）、复杂文档场景关键信息抽取（解决生僻字、特殊标点、多页pdf、表格等难点问题）、通用OCR、文档场景专用OCR、通用表格识别。针对垂类业务场景，也支持模型训练、微调和Prompt优化。
 - **🔥2023.8.7 发布 PaddleOCR [release/2.7](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.7)**
     - 发布[PP-OCRv4](./doc/doc_ch/PP-OCRv4_introduction.md)，提供mobile和server两种模型
       - PP-OCRv4-mobile：速度可比情况下，中文场景效果相比于PP-OCRv3再提升4.5%，英文场景提升10%，80语种多语言模型平均识别准确率提升8%以上
@@ -41,12 +43,12 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
   - [表格识别](./ppstructure/table/README_ch.md)模型优化：设计3大优化策略，预测耗时不变情况下，模型精度提升6%；
   - [关键信息抽取](./ppstructure/kie/README_ch.md)模型优化：设计视觉无关模型结构，语义实体识别精度提升2.8%，关系抽取精度提升9.1%。
 - 🔥**2022.8 发布 [OCR场景应用集合](./applications)**：包含数码管、液晶屏、车牌、高精度SVTR模型、手写体识别等**9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。
-  
+
 > [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)、[PP-Structure](./ppstructure/README_ch.md)和[PP-ChatOCR](https://aistudio.baidu.com/aistudio/projectdetail/6488689)，并打通数据生产、模型训练、压缩、预测部署全流程。
+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)、[PP-Structure](./ppstructure/README_ch.md)和[PP-ChatOCRv2](https://aistudio.baidu.com/projectdetail/paddlex/7050167)，并打通数据生产、模型训练、压缩、预测部署全流程。
 
 <div align="center">
     <img src="https://raw.githubusercontent.com/tink2123/test/master/ppocrv4.png">
@@ -57,28 +59,23 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 ## ⚡ 快速开始
 
-- 在线网站体验：
-    - PP-OCRv4 在线体验地址：https://aistudio.baidu.com/aistudio/projectdetail/6611435
-    - PP-ChatOCR 在线体验地址：https://aistudio.baidu.com/aistudio/projectdetail/6488689
+- 在线免费体验：
+    - PP-OCRv4 在线体验地址：https://aistudio.baidu.com/application/detail/7658
+    - PP-ChatOCRv2 在线体验地址：https://aistudio.baidu.com/application/detail/10368
+
 - 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)
-- 飞桨AI套件（PaddleX）中训练、推理、高性能部署全流程体验：
-    - PP-OCRv4：https://aistudio.baidu.com/aistudio/modelsdetail?modelId=286
-    - PP-ChatOCR：https://aistudio.baidu.com/aistudio/modelsdetail?modelId=332
 - 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)
 
 <a name="技术交流合作"></a>
 ## 📖 技术交流合作
 - 飞桨AI套件([PaddleX](http://10.136.157.23:8080/paddle/paddleX))提供了飞桨模型训压推一站式全流程高效率开发平台，其使命是助力AI技术快速落地，愿景是使人人成为AI Developer！
-   - PaddleX 目前覆盖图像分类、目标检测、图像分割、3D、OCR和时序预测等领域方向，已内置了36种基础单模型，例如RP-DETR、PP-YOLOE、PP-HGNet、PP-LCNet、PP-LiteSeg等；集成了12种实用的产业方案，例如PP-OCRv4、PP-ChatOCR、PP-ShiTu、PP-TS、车载路面垃圾检测、野生动物违禁制品识别等。
+   - PaddleX 目前覆盖图像分类、目标检测、图像分割、3D、OCR和时序预测等领域方向，已内置了36种基础单模型，例如RT-DETR、PP-YOLOE、PP-HGNet、PP-LCNet、PP-LiteSeg等；集成了12种实用的产业方案，例如PP-OCRv4、PP-ChatOCR、PP-ShiTu、PP-TS、车载路面垃圾检测、野生动物违禁制品识别等。
    - PaddleX 提供了“工具箱”和“开发者”两种AI开发模式。工具箱模式可以无代码调优关键超参，开发者模式可以低代码进行单模型训压推和多模型串联推理，同时支持云端和本地端。
    - PaddleX 还支持联创开发，利润分成！目前 PaddleX 正在快速迭代，欢迎广大的个人开发者和企业开发者参与进来，共创繁荣的 AI 技术生态！
 
-微信扫描下面二维码添加运营同学，并回复【paddlex】，运营同学会邀请您加入官方交流群，获得更高效的问题答疑。
+- PaddleX官网地址：https://aistudio.baidu.com/intro/paddlex
 
-<div align="center">
-<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus_paddlex.jpg"  width = "150" height = "150",caption='' />
-<p>飞桨AI套件【PaddleX】技术交流群二维码</p>
-</div>
+- PaddleX官方交流频道：https://aistudio.baidu.com/community/channel/610
 
 <a name="电子书"></a>
 ## 📚《动手学OCR》电子书
@@ -99,7 +96,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 | 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-OCRv4模型（15.8M）     | ch_PP-OCRv4_xx          | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_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-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_train.tar) |
+| 中英文超轻量PP-OCRv4模型（15.8M）     | ch_PP-OCRv4_xx          | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_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-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_train.tar) |
 | 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) |