Merge pull request #77 from RapidAI/add_link_for_doc_crection

chore: add link for doc distortion correction

Author: Joker1212

README.md

@@ -17,14 +17,14 @@
 ### 最近更新
 - **2024.10.22**
   - 补充复杂背景多表格检测提取方案[RapidTableDet](https://github.com/RapidAI/RapidTableDetection)
-- **2024.10.29**
-  - 使用yolo11重新训练表格分类器，修正wired_table_rec v2逻辑坐标还原错误，并更新测评
 - **2024.11.12**
   - 抽离模型识别和处理过程核心阈值，方便大家进行微调适配自己的场景[微调入参参考](#核心参数)   
+- **2024.11.16**
+  - 补充文档扭曲矫正方案，可作为前置处理 [文档扭曲变形修正](https://github.com/Joker1212/RapidUnWrap)
 
 ### 简介
 💖该仓库是用来对文档中表格做结构化识别的推理库，包括来自阿里读光有线和无线表格识别模型，llaipython(微信)贡献的有线表格模型，网易Qanything内置表格分类模型等。\
-[快速开始](#安装) [模型评测](#指标结果) [使用建议](#使用建议) [表格旋转及透视修正](#表格旋转及透视修正) [微调入参参考](#核心参数) [常见问题](#FAQ) [更新计划](#更新计划)
+[快速开始](#安装) [模型评测](#指标结果) [使用建议](#使用建议) [文档扭曲变形修正](https://github.com/Joker1212/RapidUnWrap) [表格旋转及透视修正](#表格旋转及透视修正) [微调入参参考](#核心参数) [常见问题](#FAQ) [更新计划](#更新计划)
 #### 特点
 
 ⚡  **快**  采用ONNXRuntime作为推理引擎，cpu下单图推理1-7s
@@ -190,6 +190,8 @@ html, elasp, polygons, logic_points, ocr_res = lineless_table_rec(
    下载更高精度的ocr模型,在执行时传入ocr_result即可, 
    - 或者尝试调节rapid_ocr的参数, 根据在线demo调节参数， [modelscope](https://www.modelscope.cn/studios/liekkas/RapidOCRDemo/summary) [huggingface](https://huggingface.co/spaces/SWHL/RapidOCRDemo)
      然后在推理时传入即可
+2. **问：文档扭曲变形怎么处理？**
+    - 答：使用 [RapidUnwrap](https://github.com/Joker1212/RapidUnWrap)
 3. **问：模型支持 gpu 加速吗？**
     - 答：目前表格模型的推理非常快，有线表格在100ms级别，无线表格在500ms级别，
       主要耗时在ocr阶段，可以参考 [rapidocr_paddle](https://rapidai.github.io/RapidOCRDocs/install_usage/rapidocr_paddle/usage/#_3)

README_en.md

@@ -16,16 +16,14 @@
 - **2024.10.22**
     - Added the complex background multi-table detection and extraction solution [RapidTableDet](https://github.com/RapidAI/RapidTableDetection).
 
-- **2024.10.29**
-    - Retrained the table classifier using YOLO11 to fix the logic coordinate restoration error in wired_table_rec v2 and updated evaluations.
-
 - **2024.11.12**
     - Extracted model recognition and processing core thresholds for easier fine-tuning according to specific scenarios. See [Core Parameters](#core-parameters).
-
+- **2024.11.16**
+    - Added document distortion correction solution, which can be used as a pre-processing step [Document Distortion Correction](https://github.com/Joker1212/RapidUnWrap)
 ### Introduction
 💖 This repository serves as an inference library for structured recognition of tables within documents, including models for wired and wireless table recognition from Alibaba DulaLight, a wired table model from llaipython (WeChat), and a built-in table classification model from NetEase Qanything.
 
-[Quick Start](#installation) [Model Evaluation](#evaluation-results) [Usage Recommendations](#usage-recommendations) [Table Rotation & Perspective Correction](#table-rotation-and-perspective-correction) [Fine-tuning Input Parameters Reference](#core-parameters) [Frequently Asked Questions](#faqs) [Update Plan](#update-plan)
+[Quick Start](#installation) [Model Evaluation](#evaluation-results) [Usage Recommendations](#usage-recommendations) [Document Distortion Correction](https://github.com/Joker1212/RapidUnWrap) [Table Rotation & Perspective Correction](#table-rotation-and-perspective-correction) [Fine-tuning Input Parameters Reference](#core-parameters) [Frequently Asked Questions](#faqs) [Update Plan](#update-plan)
 #### Features
 
 ⚡ **Fast:** Uses ONNXRuntime as the inference engine, achieving 1-7 seconds per image on CPU.
@@ -203,7 +201,10 @@ html, elasp, polygons, logic_points, ocr_res = lineless_table_rec(
 ### Processing Workflow
 
 ```mermaid
-A[/Table Image/] --> B([Table Classification table_cls]) B --> C([Wired Table Recognition wired_table_rec]) & D([Wireless Table Recognition lineless_table_rec]) --> E([Text Recognition rapidocr_onnxruntime]) E --> F[/HTML Structured Output/]
+flowchart TD
+    A[/table image/] --> B([table cls table_cls])
+    B --> C([wired_table_rec]) & D([lineless_table_rec]) --> E([rapidocr_onnxruntime])
+    E --> F[/html output/]
 ```
 
 ### Acknowledgments