docs: remove ppstructure old doc files (#14161)

* docs: fix multilingual doc correspondence issues * docs: remove ppstructure old doc files
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diff --git a/docs/ppocr/model_train/tricks.en.md b/docs/ppocr/model_train/tricks.en.md
@@ -2,8 +2,6 @@
 comments: true
 ---
 
-### Tricks
-
 Here we have sorted out some Chinese OCR training and prediction tricks, which are being updated continuously. You are welcome to contribute more OCR tricks ~
 
 #### 1、Replace Backbone Network

diff --git a/docs/ppocr/model_train/tricks.md b/docs/ppocr/model_train/tricks.md
@@ -2,8 +2,6 @@
 comments: true
 ---
 
-### 技巧
-
 这里我们整理了一些中文OCR训练和预测技巧，持续更新中，欢迎大家贡献更多OCR技巧~
 
 #### 1、更换骨干网络

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -119,7 +119,7 @@ plugins:
             文本方向分类器: Text Angle Classification
             关键信息提取: Key Information Extraction
             模型微调: Fine-tune
-            训练tips: Training Tricks
+            训练tricks: Training Tricks
             模型压缩: Model Compression
             模型量化: Model Quantization
             模型裁剪: Model Prune

diff --git a/ppstructure/README.md b/ppstructure/README.md
@@ -1,120 +1 @@
-English | [简体中文](README_ch.md)
-
-# PP-Structure
-
-- [1. Introduction](#1-introduction)
-- [2. Features](#2-features)
-- [3. Results](#3-results)
-  - [3.1 Layout analysis and table recognition](#31-layout-analysis-and-table-recognition)
-  - [3.2 Layout Recovery](#32-layout-recovery)
-  - [3.3 KIE](#33-kie)
-- [4. Quick start](#4-quick-start)
-- [5. Model List](#5-model-list)
-
-## 1. Introduction
-
-PP-Structure is an intelligent document analysis system developed by the PaddleOCR team, which aims to help developers better complete tasks related to document understanding such as layout analysis and table recognition.
-
-The pipeline of PP-StructureV2 system is shown below. The document image first passes through the image direction correction module to identify the direction of the entire image and complete the direction correction. Then, two tasks of layout information analysis and key information extraction can be completed.
-
-- In the layout analysis task, the image first goes through the layout analysis model to divide the image into different areas such as text, table, and figure, and then analyze these areas separately. For example, the table area is sent to the form recognition module for structured recognition, and the text area is sent to the OCR engine for text recognition. Finally, the layout recovery module restores it to a word or pdf file with the same layout as the original image;
-- In the key information extraction task, the OCR engine is first used to extract the text content, and then the SER(semantic entity recognition) module obtains the semantic entities in the image, and finally the RE(relationship extraction) module obtains the correspondence between the semantic entities, thereby extracting the required key information.
-<img src="https://user-images.githubusercontent.com/14270174/195265734-6f4b5a7f-59b1-4fcc-af6d-89afc9bd51e1.jpg" width="100%"/>
-
-More technical details: 👉 [PP-StructureV2 Technical Report](https://arxiv.org/abs/2210.05391)
-
-PP-StructureV2 supports independent use or flexible collocation of each module. For example, you can use layout analysis alone or table recognition alone. Click the corresponding link below to get the tutorial for each independent module:
-
-- [Layout Analysis](layout/README.md)
-- [Table Recognition](table/README.md)
-- [Key Information Extraction](kie/README.md)
-- [Layout Recovery](recovery/README.md)
-
-## 2. Features
-
-The main features of PP-StructureV2 are as follows:
-- Support layout analysis of documents in the form of images/pdfs, which can be divided into areas such as **text, titles, tables, figures, formulas, etc.**;
-- Support common Chinese and English **table detection** tasks;
-- Support structured table recognition, and output the final result to **Excel file**;
-- Support multimodal-based Key Information Extraction (KIE) tasks - **Semantic Entity Recognition** (SER) and **Relation Extraction (RE);
-- Support **layout recovery**, that is, restore the document in word or pdf format with the same layout as the original image;
-- Support customized training and multiple inference deployment methods such as python whl package quick start;
-- Connect with the semi-automatic data labeling tool PPOCRLabel, which supports the labeling of layout analysis, table recognition, and SER.
-
-## 3. Results
-
-PP-StructureV2 supports the independent use or flexible collocation of each module. For example, layout analysis can be used alone, or table recognition can be used alone. Only the visualization effects of several representative usage methods are shown here.
-
-### 3.1 Layout analysis and table recognition
-
-The figure shows the pipeline of layout analysis + table recognition. The image is first divided into four areas of image, text, title and table by layout analysis, and then OCR detection and recognition is performed on the three areas of image, text and title, and the table is performed table recognition, where the image will also be stored for use.
-<img src="docs/table/ppstructure.GIF" width="100%"/>
-
-### 3.1.1 版面识别返回单字坐标
-The following figure shows the result of layout analysis on single word， please refer to the [doc](./return_word_pos.md)。
-![show_0_mdf_v2](https://github.com/PaddlePaddle/PaddleOCR/assets/43341135/799450d4-d2c5-4b61-b490-e160dc0f515c)
-
-### 3.2 Layout recovery
-
-The following figure shows the effect of layout recovery based on the results of layout analysis and table recognition in the previous section.
-<img src="./docs/recovery/recovery.jpg" width="100%"/>
-
-### 3.3 KIE
-
-* SER
-
-Different colored boxes in the figure represent different categories.
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/197464552-69de557f-edff-4c7f-acbf-069df1ba097f.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186095702-9acef674-12af-4d09-97fc-abf4ab32600e.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185539141-68e71c75-5cf7-4529-b2ca-219d29fa5f68.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185310636-6ce02f7c-790d-479f-b163-ea97a5a04808.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185539517-ccf2372a-f026-4a7c-ad28-c741c770f60a.png" width="600">
-</div>
-
-* RE
-
-In the figure, the red box represents `Question`, the blue box represents `Answer`, and `Question` and `Answer` are connected by green lines.
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186094813-3a8e16cc-42e5-4982-b9f4-0134dfb5688d.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186095641-5843b4da-34d7-4c1c-943a-b1036a859fe3.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185393805-c67ff571-cf7e-4217-a4b0-8b396c4f22bb.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185540080-0431e006-9235-4b6d-b63d-0b3c6e1de48f.jpg" width="600">
-</div>
-
-## 4. Quick start
-
-Start from [Quick Start](./docs/quickstart_en.md).
-
-## 5. Model List
-
-Some tasks need to use both the structured analysis models and the OCR models. For example, the table recognition task needs to use the table recognition model for structured analysis, and the OCR model to recognize the text in the table. Please select the appropriate models according to your specific needs.
-
-For structural analysis related model downloads, please refer to:
-- [PP-Structure Model Zoo](./docs/models_list_en.md)
-
-For OCR related model downloads, please refer to:
-- [PP-OCR Model Zoo](../doc/doc_en/models_list_en.md)
+See [Docs](https://paddlepaddle.github.io/PaddleOCR/latest/en/ppstructure/overview.html) for details.
diff --git a/ppstructure/README_ch.md b/ppstructure/README_ch.md
@@ -1,126 +1 @@
-[English](README.md) | 简体中文
-
-# PP-Structure 文档分析
-
-- [1. 简介](#1)
-- [2. 特性](#2)
-- [3. 效果展示](#3)
-  - [3.1 版面分析和表格识别](#31)
-  - [3.2 版面恢复](#32)
-  - [3.3 关键信息抽取](#33)
-- [4. 快速体验](#4)
-- [5. 模型库](#5)
-
-<a name="1"></a>
-## 1. 简介
-
-PP-Structure是PaddleOCR团队自研的智能文档分析系统，旨在帮助开发者更好的完成版面分析、表格识别等文档理解相关任务。
-
-PP-StructureV2系统流程图如下所示，文档图像首先经过图像矫正模块，判断整图方向并完成转正，随后可以完成版面信息分析与关键信息抽取2类任务。
-- 版面分析任务中，图像首先经过版面分析模型，将图像划分为文本、表格、图像等不同区域，随后对这些区域分别进行识别，如，将表格区域送入表格识别模块进行结构化识别，将文本区域送入OCR引擎进行文字识别，最后使用版面恢复模块将其恢复为与原始图像布局一致的word或者pdf格式的文件；
-- 关键信息抽取任务中，首先使用OCR引擎提取文本内容，然后由语义实体识别模块获取图像中的语义实体，最后经关系抽取模块获取语义实体之间的对应关系，从而提取需要的关键信息。
-
-<img src="https://user-images.githubusercontent.com/14270174/195265734-6f4b5a7f-59b1-4fcc-af6d-89afc9bd51e1.jpg" width="100%"/>
-
-更多技术细节：👉 PP-StructureV2技术报告 [中文版](docs/PP-StructureV2_introduction.md)，[英文版](https://arxiv.org/abs/2210.05391)。
-
-PP-StructureV2支持各个模块独立使用或灵活搭配，如，可以单独使用版面分析，或单独使用表格识别，点击下面相应链接获取各个独立模块的使用教程：
-
-- [版面分析](layout/README_ch.md)
-- [表格识别](table/README_ch.md)
-- [关键信息抽取](kie/README_ch.md)
-- [版面复原](recovery/README_ch.md)
-
-<a name="2"></a>
-## 2. 特性
-
-PP-StructureV2的主要特性如下：
-- 支持对图片/pdf形式的文档进行版面分析，可以划分**文字、标题、表格、图片、公式等**区域；
-- 支持通用的中英文**表格检测**任务；
-- 支持表格区域进行结构化识别，最终结果输出**Excel文件**；
-- 支持基于多模态的关键信息抽取(Key Information Extraction，KIE)任务-**语义实体识别**(Semantic Entity Recognition，SER)和**关系抽取**(Relation Extraction，RE)；
-- 支持**版面复原**，即恢复为与原始图像布局一致的word或者pdf格式的文件；
-- 支持自定义训练及python whl包调用等多种推理部署方式，简单易用；
-- 与半自动数据标注工具PPOCRLabel打通，支持版面分析、表格识别、SER三种任务的标注。
-
-<a name="3"></a>
-## 3. 效果展示
-PP-StructureV2支持各个模块独立使用或灵活搭配，如，可以单独使用版面分析，或单独使用表格识别，这里仅展示几种代表性使用方式的可视化效果。
-
-<a name="31"></a>
-### 3.1 版面分析和表格识别
-下图展示了版面分析+表格识别的整体流程，图片先有版面分析划分为图像、文本、标题和表格四种区域，然后对图像、文本和标题三种区域进行OCR的检测识别，对表格进行表格识别，其中图像还会被存储下来以便使用。
-<img src="./docs/table/ppstructure.GIF" width="100%"/>
-
-### 3.1.1 版面识别返回单字坐标
-下图展示了基于上一节版面分析对文字进行定位的效果， 可参考[文档](./return_word_pos.md)。
-![show_0_mdf_v2](https://github.com/PaddlePaddle/PaddleOCR/assets/43341135/799450d4-d2c5-4b61-b490-e160dc0f515c)
-
-
-<a name="32"></a>
-### 3.2 版面恢复
-下图展示了基于上一节版面分析和表格识别的结果进行版面恢复的效果。
-<img src="./docs/recovery/recovery.jpg" width="100%"/>
-
-<a name="33"></a>
-### 3.3 关键信息抽取
-
-* SER
-
-图中不同颜色的框表示不同的类别。
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185539141-68e71c75-5cf7-4529-b2ca-219d29fa5f68.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185310636-6ce02f7c-790d-479f-b163-ea97a5a04808.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185539517-ccf2372a-f026-4a7c-ad28-c741c770f60a.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/197464552-69de557f-edff-4c7f-acbf-069df1ba097f.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186095702-9acef674-12af-4d09-97fc-abf4ab32600e.png" width="600">
-</div>
-
-* RE
-
-图中红色框表示`问题`，蓝色框表示`答案`，`问题`和`答案`之间使用绿色线连接。
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185393805-c67ff571-cf7e-4217-a4b0-8b396c4f22bb.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/14270174/185540080-0431e006-9235-4b6d-b63d-0b3c6e1de48f.jpg" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186094813-3a8e16cc-42e5-4982-b9f4-0134dfb5688d.png" width="600">
-</div>
-
-<div align="center">
-    <img src="https://user-images.githubusercontent.com/25809855/186095641-5843b4da-34d7-4c1c-943a-b1036a859fe3.png" width="600">
-</div>
-
-<a name="4"></a>
-## 4. 快速体验
-
-请参考[快速使用](./docs/quickstart.md)教程。
-
-<a name="5"></a>
-## 5. 模型库
-
-部分任务需要同时用到结构化分析模型和OCR模型，如表格识别需要使用表格识别模型进行结构化解析，同时也要用到OCR模型对表格内的文字进行识别，请根据具体需求选择合适的模型。
-
-结构化分析相关模型下载可以参考：
-- [PP-Structure 模型库](./docs/models_list.md)
-
-OCR相关模型下载可以参考：
-- [PP-OCR 模型库](../doc/doc_ch/models_list.md)
+请移步[Docs](https://paddlepaddle.github.io/PaddleOCR/latest/ppstructure/overview.html)查看。