Update README

README.md

@@ -12,9 +12,22 @@
 </div>
 
 ### Introduction
+This repo is an inference library used for structured recognition of tables in documents, including table structure recognition algorithm models from PaddleOCR, wired and wireless table recognition algorithm models from Alibaba Duguang, etc.
+
+The repo has improved the pre- and post-processing of form recognition and combined with OCR to ensure that the form recognition part can be used directly.
 
 ### What is Table Structure Recognition?
+Table Structure Recognition (TSR) aims to extract the logical or physical structure of table images, thereby converting unstructured table images into machine-readable formats.
+
+Logical structure: represents the row/column relationship of cells (such as the same row, the same column) and the span information of cells.
+
+Physical structure: includes not only the logical structure, but also the cell's bounding box, content and other information, emphasizing the physical location of the cell.
 
+<div align='center'>
+   <img src="https://github.com/RapidAI/TableStructureRec/releases/download/v0.0.0/TSRFramework.jpg">
+</div>
+
+Figure from: [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.html)
 
 ### Documentation
 Full documentation can be found on [docs](https://rapidai.github.io/TableStructureRec/docs/), in Chinese.
@@ -26,17 +39,12 @@ Full documentation can be found on [docs](https://rapidai.github.io/TableStructu
 
 [LORE](https://www.modelscope.cn/models/damo/cv_resnet-transformer_table-structure-recognition_lore/summary)
 
-### Code Contributors
-<p align="left">
-  <a href="https://github.com/RapidAI/TableStructureRec/graphs/contributors">
-    <img src="https://contrib.rocks/image?repo=RapidAI/TableStructureRec" width="8%"/>
-  </a>
-</p>
 
 ### Contributing
-- Pull requests are welcome. For major changes, please open an issue first
+Pull requests are welcome. For major changes, please open an issue first
 to discuss what you would like to change.
-- Please make sure to update tests as appropriate.
+
+Please make sure to update tests as appropriate.
 
 ### [Sponsor](https://rapidai.github.io/Knowledge-QA-LLM/docs/sponsor/)
 If you want to sponsor the project, you can directly click the **Buy me a coffee** image, please write a note (e.g. your github account name) to facilitate adding to the sponsorship list below.

docs/README_zh.md

@@ -14,10 +14,20 @@
 ### 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PaddleOCR的表格结构识别算法模型、来自阿里读光有线和无线表格识别算法模型等。
 
-该仓库将表格识别前后处理做了完善，并结合OCR，保证表格识别部分可用。
+该仓库将表格识别前后处理做了完善，并结合OCR，保证表格识别部分可直接使用。
 
 ### 表格结构化识别
+表格结构识别（Table Structure Recognition, TSR）旨在提取表格图像的逻辑或物理结构，从而将非结构化的表格图像转换为机器可读的格式。
 
+逻辑结构：表示单元格的行/列关系（例如同行、同列）和单元格的跨度信息。
+
+物理结构：不仅包含逻辑结构，还包含单元格的包围框、内容等信息，强调单元格的物理位置。
+
+<div align='center'>
+  <img src="https://github.com/RapidAI/TableStructureRec/releases/download/v0.0.0/TSRFramework.jpg">
+</div>
+
+图来自： [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.html)
 
 ### 文档
 完整文档请移步：[docs](https://rapidai.github.io/TableStructureRec/docs/)
@@ -29,12 +39,6 @@
 
 [读光-表格结构识别-无线表格](https://www.modelscope.cn/models/damo/cv_resnet-transformer_table-structure-recognition_lore/summary)
 
-### 贡献者
-<p align="left">
-  <a href="https://github.com/RapidAI/TableStructureRec/graphs/contributors">
-    <img src="https://contrib.rocks/image?repo=RapidAI/TableStructureRec" width="8%"/>
-  </a>
-</p>
 
 ### 贡献指南
 欢迎提交请求。对于重大更改，请先打开issue讨论您想要改变的内容。