Fix docs (#811)

* fix mindocr static webpage

* fix lint

---------

Co-authored-by: slgao <[email protected]>

Author: Bruce-py

.github/workflows/docs.yml

@@ -21,6 +21,10 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements/docs.txt
+      - name: Make the script executable
+        run: chmod +x docs/replace_path.sh
+      - name: Run the scripts
+        run: ./docs/replace_path.sh
       - name: Build site
         run: mkdocs build
       - name: Deploy to gh-pages

README.md

@@ -274,7 +274,7 @@ You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Thi
 <summary>Key Information Extraction</summary>
 
 - [x] [LayoutXLM](configs/kie/vi_layoutxlm/README.md) (arXiv'2021)
-- [x] [LayoutLMv3](configs/kie/layoutlmv3/README.md) (arXiv'2022)
+- [x] [LayoutLMv3](configs/layout/layoutlmv3/README.md) (arXiv'2022)
 
 </details>
 
@@ -292,7 +292,7 @@ You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Thi
 
 </details>
 
-For the detailed performance of the trained models, please refer to [https://github.com/mindspore-lab/mindocr/blob/main/configs](./configs).
+For the detailed performance of the trained models, please refer to [configs](configs).
 
 For details of MindSpore Lite inference models support, please refer to [MindOCR Models Support List](docs/en/inference/mindocr_models_list.md) and [Third-party Models Support List](docs/en/inference/thirdparty_models_list.md) (PaddleOCR etc.).
 
@@ -306,45 +306,45 @@ MindOCR provides a [dataset conversion tool](https://github.com/mindspore-lab/mi
 - [Born-Digital Images](https://rrc.cvc.uab.es/?ch=1) [[download](docs/en/datasets/borndigital.md)]
 - [CASIA-10K](http://www.nlpr.ia.ac.cn/pal/CASIA10K.html) [[download](docs/en/datasets/casia10k.md)]
 - [CCPD](https://github.com/detectRecog/CCPD) [[download](docs/en/datasets/ccpd.md)]
-- [Chinese Text Recognition Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) [[paper](https://arxiv.org/abs/2112.15093)] [[download](docs/en/datasets/chinese_text_recognition.md)]
+- [Chinese Text Recognition Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) [[paper](https://arxiv.org/abs/2112.15093)] \[[download](docs/en/datasets/chinese_text_recognition.md)]
 - [COCO-Text](https://rrc.cvc.uab.es/?ch=5) [[download](docs/en/datasets/cocotext.md)]
 - [CTW](https://ctwdataset.github.io/) [[download](docs/en/datasets/ctw.md)]
-- [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) [[paper](https://rrc.cvc.uab.es/files/short_rrc_2015.pdf)] [[download](docs/en/datasets/icdar2015.md)]
+- [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) [[paper](https://rrc.cvc.uab.es/files/short_rrc_2015.pdf)] \[[download](docs/en/datasets/icdar2015.md)]
 - [ICDAR2019 ArT](https://rrc.cvc.uab.es/?ch=14) [[download](docs/en/datasets/ic19_art.md)]
 - [LSVT](https://rrc.cvc.uab.es/?ch=16) [[download](docs/en/datasets/lsvt.md)]
-- [MLT2017](https://rrc.cvc.uab.es/?ch=8) [[paper](https://ieeexplore.ieee.org/abstract/document/8270168)] [[download](docs/en/datasets/mlt2017.md)]
-- [MSRA-TD500](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500)) [[paper](https://ieeexplore.ieee.org/abstract/document/6247787)] [[download](docs/en/datasets/td500.md)]
+- [MLT2017](https://rrc.cvc.uab.es/?ch=8) [[paper](https://ieeexplore.ieee.org/abstract/document/8270168)] \[[download](docs/en/datasets/mlt2017.md)]
+- [MSRA-TD500](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500)) [[paper](https://ieeexplore.ieee.org/abstract/document/6247787)] \[[download](docs/en/datasets/td500.md)]
 - [MTWI-2018](https://tianchi.aliyun.com/competition/entrance/231651/introduction) [[download](docs/en/datasets/mtwi2018.md)]
 - [RCTW-17](https://rctw.vlrlab.net/) [[download](docs/en/datasets/rctw17.md)]
 - [ReCTS](https://rrc.cvc.uab.es/?ch=12) [[download](docs/en/datasets/rects.md)]
-- [SCUT-CTW1500](https://github.com/Yuliang-Liu/Curve-Text-Detector) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319300664)] [[download](docs/en/datasets/ctw1500.md)]
+- [SCUT-CTW1500](https://github.com/Yuliang-Liu/Curve-Text-Detector) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319300664)] \[[download](docs/en/datasets/ctw1500.md)]
 - [SROIE](https://rrc.cvc.uab.es/?ch=13) [[download](docs/en/datasets/sroie.md)]
 - [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) [[download](docs/en/datasets/svt.md)]
-- [SynText150k](https://github.com/aim-uofa/AdelaiDet) [[paper](https://arxiv.org/abs/2002.10200)] [[download](docs/en/datasets/syntext150k.md)]
-- [SynthText](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) [[paper](https://www.robots.ox.ac.uk/~vgg/publications/2016/Gupta16/)] [[download](docs/en/datasets/synthtext.md)]
+- [SynText150k](https://github.com/aim-uofa/AdelaiDet) [[paper](https://arxiv.org/abs/2002.10200)] \[[download](docs/en/datasets/syntext150k.md)]
+- [SynthText](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) [[paper](https://www.robots.ox.ac.uk/~vgg/publications/2016/Gupta16/)] \[[download](docs/en/datasets/synthtext.md)]
 - [TextOCR](https://textvqa.org/textocr/) [[download](docs/en/datasets/textocr.md)]
-- [Total-Text](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) [[paper](https://arxiv.org/abs/1710.10400)] [[download](docs/en/datasets/totaltext.md)]
+- [Total-Text](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) [[paper](https://arxiv.org/abs/1710.10400)] \[[download](docs/en/datasets/totaltext.md)]
 
 </details>
 
 <details close markdown>
 <summary>Layout Analysis Datasets</summary>
 
-- [PublayNet](https://github.com/ibm-aur-nlp/PubLayNet) [[paper](https://arxiv.org/abs/1908.07836)] [[download](https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz)]
+- [PublayNet](https://github.com/ibm-aur-nlp/PubLayNet) [[paper](https://arxiv.org/abs/1908.07836)] \[[download](https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz)]
 
 </details>
 
 <details close markdown>
 <summary>Key Information Extraction Datasets</summary>
 
-- [XFUND](https://github.com/doc-analysis/XFUND) [[paper](https://aclanthology.org/2022.findings-acl.253/)] [[download](https://github.com/doc-analysis/XFUND/releases/tag/v1.0)]
+- [XFUND](https://github.com/doc-analysis/XFUND) [[paper](https://aclanthology.org/2022.findings-acl.253/)] \[[download](https://github.com/doc-analysis/XFUND/releases/tag/v1.0)]
 
 </details>
 
 <details close markdown>
 <summary>Table Recognition Datasets</summary>
 
-- [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) [[paper](https://arxiv.org/pdf/1911.10683.pdf)] [[download](https://dax-cdn.cdn.appdomain.cloud/dax-pubtabnet/2.0.0/pubtabnet.tar.gz)]
+- [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) [[paper](https://arxiv.org/pdf/1911.10683.pdf)] \[[download](https://dax-cdn.cdn.appdomain.cloud/dax-pubtabnet/2.0.0/pubtabnet.tar.gz)]
 
 </details>
 
@@ -362,7 +362,7 @@ Frequently asked questions about configuring environment and mindocr, please ref
 
 - 2023/04/01
 1. Add new trained models
-    - [LayoutLMv3](configs/kie/layoutlmv3/) for key information extraction
+    - [LayoutLMv3](configs/layout/layoutlmv3/) for key information extraction
 
 - 2024/03/20
 1. Add new trained models

README_CN.md

@@ -30,7 +30,7 @@
 
 <!--start-->
 ## 简介
-MindOCR是一个基于[MindSpore](https://www.mindspore.cn/en) 框架开发的OCR开源工具箱，集成系列主流文字检测识别的算法、模型，并提供易用的训练和推理工具，可以帮助用户快速开发和应用业界SoTA文本检测、文本识别模型，如DBNet/DBNet++和CRNN/SVTR，满足图像文档理解的需求。
+MindOCR是一个基于[MindSpore](https://www.mindspore.cn/) 框架开发的OCR开源工具箱，集成系列主流文字检测识别的算法、模型，并提供易用的训练和推理工具，可以帮助用户快速开发和应用业界SoTA文本检测、文本识别模型，如DBNet/DBNet++和CRNN/SVTR，满足图像文档理解的需求。
 
 
 <details open markdown>
@@ -219,9 +219,9 @@ python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_img
 
 ### 3. 模型离线推理
 
-你可以在MindOCR中对**MindOCR原生模型**或**第三方模型**（如PaddleOCR、MMOCR等）进行MindSpore Lite推理。详情请参考[模型离线推理教程](docs/zh/inference/inference_tutorial.md)。
+你可以在MindOCR中对 **MindOCR原生模型** 或 **第三方模型**（如PaddleOCR、MMOCR等）进行MindSpore Lite推理。详情请参考[模型离线推理教程](docs/zh/inference/inference_tutorial.md)。
 
-## 使用教程
+## <span id="使用教程">使用教程</span>
 
 - 数据集
     - [数据集准备](docs/zh/datasets/converters.md)
@@ -275,7 +275,7 @@ python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_img
 <summary>关键信息抽取</summary>
 
 - [x] [LayoutXLM](configs/kie/vi_layoutxlm/README_CN.md) (arXiv'2021)
-- [x] [LayoutLMv3](configs/kie/layoutlmv3/README_CN.md) (arXiv'2022)
+- [x] [LayoutLMv3](configs/layout/layoutlmv3/README_CN.md) (arXiv'2022)
 
 </details>
 
@@ -294,7 +294,7 @@ python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_img
 </details>
 
 
-关于以上模型的具体训练方法和结果，请参见[configs](https://github.com/mindspore-lab/mindocr/blob/main/configs)下各模型子目录的readme文档。
+关于以上模型的具体训练方法和结果，请参见[configs](configs)下各模型子目录的readme文档。
 
 [MindSpore Lite](https://www.mindspore.cn/lite)模型推理的支持列表，
 请参见[MindOCR原生模型推理支持列表](docs/zh/inference/mindocr_models_list.md) 和 [第三方模型推理支持列表](docs/zh/inference/thirdparty_models_list.md)（如PaddleOCR）。
@@ -310,45 +310,45 @@ MindOCR提供了[数据格式转换工具](https://github.com/mindspore-lab/mind
 - [Born-Digital Images](https://rrc.cvc.uab.es/?ch=1) [[download](docs/zh/datasets/borndigital.md)]
 - [CASIA-10K](http://www.nlpr.ia.ac.cn/pal/CASIA10K.html) [[download](docs/zh/datasets/casia10k.md)]
 - [CCPD](https://github.com/detectRecog/CCPD) [[download](docs/zh/datasets/ccpd.md)]
-- [Chinese Text Recognition Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) [[paper](https://arxiv.org/abs/2112.15093)] [[download](docs/zh/datasets/chinese_text_recognition.md)]
+- [Chinese Text Recognition Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition) [[paper](https://arxiv.org/abs/2112.15093)] \[[download](docs/zh/datasets/chinese_text_recognition.md)]
 - [COCO-Text](https://rrc.cvc.uab.es/?ch=5) [[download](docs/zh/datasets/cocotext.md)]
 - [CTW](https://ctwdataset.github.io/) [[download](docs/zh/datasets/ctw.md)]
-- [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) [[paper](https://rrc.cvc.uab.es/files/short_rrc_2015.pdf)] [[download](docs/zh/datasets/icdar2015.md)]
+- [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) [[paper](https://rrc.cvc.uab.es/files/short_rrc_2015.pdf)] \[[download](docs/zh/datasets/icdar2015.md)]
 - [ICDAR2019 ArT](https://rrc.cvc.uab.es/?ch=14) [[download](docs/zh/datasets/ic19_art.md)]
 - [LSVT](https://rrc.cvc.uab.es/?ch=16) [[download](docs/zh/datasets/lsvt.md)]
-- [MLT2017](https://rrc.cvc.uab.es/?ch=8) [[paper](https://ieeexplore.ieee.org/abstract/document/8270168)] [[download](docs/zh/datasets/mlt2017.md)]
-- [MSRA-TD500](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500)) [[paper](https://ieeexplore.ieee.org/abstract/document/6247787)] [[download](docs/zh/datasets/td500.md)]
+- [MLT2017](https://rrc.cvc.uab.es/?ch=8) [[paper](https://ieeexplore.ieee.org/abstract/document/8270168)] \[[download](docs/zh/datasets/mlt2017.md)]
+- [MSRA-TD500](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500)) [[paper](https://ieeexplore.ieee.org/abstract/document/6247787)] \[[download](docs/zh/datasets/td500.md)]
 - [MTWI-2018](https://tianchi.aliyun.com/competition/entrance/231651/introduction) [[download](docs/zh/datasets/mtwi2018.md)]
 - [RCTW-17](https://rctw.vlrlab.net/) [[download](docs/zh/datasets/rctw17.md)]
 - [ReCTS](https://rrc.cvc.uab.es/?ch=12) [[download](docs/zh/datasets/rects.md)]
-- [SCUT-CTW1500](https://github.com/Yuliang-Liu/Curve-Text-Detector) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319300664)] [[download](docs/zh/datasets/ctw1500.md)]
+- [SCUT-CTW1500](https://github.com/Yuliang-Liu/Curve-Text-Detector) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319300664)] \[[download](docs/zh/datasets/ctw1500.md)]
 - [SROIE](https://rrc.cvc.uab.es/?ch=13) [[download](docs/zh/datasets/sroie.md)]
 - [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) [[download](docs/zh/datasets/svt.md)]
-- [SynText150k](https://github.com/aim-uofa/AdelaiDet) [[paper](https://arxiv.org/abs/2002.10200)] [[download](docs/zh/datasets/syntext150k.md)]
-- [SynthText](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) [[paper](https://www.robots.ox.ac.uk/~vgg/publications/2016/Gupta16/)] [[download](docs/zh/datasets/synthtext.md)]
+- [SynText150k](https://github.com/aim-uofa/AdelaiDet) [[paper](https://arxiv.org/abs/2002.10200)] \[[download](docs/zh/datasets/syntext150k.md)]
+- [SynthText](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) [[paper](https://www.robots.ox.ac.uk/~vgg/publications/2016/Gupta16/)] \[[download](docs/zh/datasets/synthtext.md)]
 - [TextOCR](https://textvqa.org/textocr/) [[download](docs/zh/datasets/textocr.md)]
-- [Total-Text](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) [[paper](https://arxiv.org/abs/1710.10400)] [[download](docs/zh/datasets/totaltext.md)]
+- [Total-Text](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) [[paper](https://arxiv.org/abs/1710.10400)] \[[download](docs/zh/datasets/totaltext.md)]
 
 </details>
 
 <details close markdown>
 <summary>版面分析数据集</summary>
 
-- [PublayNet](https://github.com/ibm-aur-nlp/PubLayNet) [[paper](https://arxiv.org/abs/1908.07836)] [[download](https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz)]
+- [PublayNet](https://github.com/ibm-aur-nlp/PubLayNet) [[paper](https://arxiv.org/abs/1908.07836)] \[[download](https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz)]
 
 </details>
 
 <details close markdown>
 <summary>关键信息抽取数据集</summary>
 
-- [XFUND](https://github.com/doc-analysis/XFUND) [[paper](https://aclanthology.org/2022.findings-acl.253/)] [[download](https://github.com/doc-analysis/XFUND/releases/tag/v1.0)]
+- [XFUND](https://github.com/doc-analysis/XFUND) [[paper](https://aclanthology.org/2022.findings-acl.253/)] \[[download](https://github.com/doc-analysis/XFUND/releases/tag/v1.0)]
 
 </details>
 
 <details close markdown>
 <summary>表格识别数据集</summary>
 
-- [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) [[paper](https://arxiv.org/pdf/1911.10683.pdf)] [[download](https://dax-cdn.cdn.appdomain.cloud/dax-pubtabnet/2.0.0/pubtabnet.tar.gz)]
+- [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) [[paper](https://arxiv.org/pdf/1911.10683.pdf)] \[[download](https://dax-cdn.cdn.appdomain.cloud/dax-pubtabnet/2.0.0/pubtabnet.tar.gz)]
 
 </details>
 
@@ -365,7 +365,7 @@ MindOCR提供了[数据格式转换工具](https://github.com/mindspore-lab/mind
 
 - 2023/04/01
 1. 增加新模型
-    - 关键信息抽取[LayoutLMv3](configs/kie/layoutlmv3/)
+    - 关键信息抽取[LayoutLMv3](configs/layout/layoutlmv3/)
 
 - 2024/03/20
 1. 增加新模型

docs/en/inference/convert_dynamic.md

@@ -1,6 +1,6 @@
 ## Inference - Dynamic Shape Scaling
 
-### 1. Introduction
+### Introduction
 
 In some inference scenarios, such as object recognition after detection, the input batch size and image size of the
 recognition network are not fixed because the number of object and the size of the object are not fixed. If each
@@ -15,11 +15,11 @@ This tool integrates the function of dataset statistics, can count the appropria
 and `width` as candidate values, and encapsulates the model conversion tool, thus realizing the automatic model shape
 scaling.
 
-### 2. Environment
+### Environment
 
 Please refer to [Environment Installation](environment.md) to install MindSpore Lite environment.
 
-### 3. Model
+### Model
 
 Currently, ONNX model files are supported, and by MindSpore Lite, they are automatically shape scaling and
 converted to MIndIR model files.
@@ -28,7 +28,7 @@ Please make sure that the input model is the dynamic shape version. For example,
 shape scaling for H and W, make sure that at least the H and W axes are dynamic, and the shape can be `(1,3,-1,-1)` and
 `(-1,3,- 1,-1) `etc.
 
-### 4. Dataset
+### Dataset
 
 Two types of data are supported:
 
@@ -48,11 +48,11 @@ Two types of data are supported:
 
    - Suitable for text recognition models
 
-#### 5. Usages
+#### Usages
 
 `cd deploy/models_utils/auto_scaling`
 
-##### 5.1 Command example
+##### Command example
 
 - auto shape scaling for batch size
 
@@ -102,7 +102,7 @@ python converter.py \
 
 The output is a single MindIR model: `model_static.mindir`
 
-##### 5.2 Parameter Details
+##### Parameter Details
 
 | Name        | Default     | Required | Description                                      |
 |:------------|:------------|:---------|:-------------------------------------------------|
@@ -114,7 +114,7 @@ The output is a single MindIR model: `model_static.mindir`
 | output_path | ./output    | N        | Path to output model                             |
 | soc_version | Ascend310P3 | N        | soc_version for Ascend，Ascend310P3 or Ascend310 |
 
-##### 5.3 Configuration file
+##### Configuration file
 
 In addition to the above command line parameters, there are some parameters in
 [auto_scaling.yaml](https://github.com/mindspore-lab/mindocr/tree/main/deploy/models_utils/auto_scaling/configs/auto_scaling.yaml) to describe the statistics of