docs: readme update

Author: Joker1212

README.md

@@ -16,31 +16,63 @@
 
 ### 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.
+This repository is a library for structured recognition of tables in documents. 
+It includes table recognition models from Paddle, Alibaba's DocLight wired and wireless table recognition models, 
+wired table models contributed by others, and the built-in table classification model from NetEase QAnything.
 
-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.
 
-The repo will continue to focus on the field of table recognition, integrate the latest and most useful table recognition algorithms, and strive to create the most valuable table recognition tool library.
 
-Welcome everyone to continue to pay attention.
+#### Features
+⚡  **Fast**: Uses ONNXRuntime as the inference engine, achieving 1-7 second inference times on CPU.
 
-### What is Table Structure Recognition?
+🎯 **Accurate**: Combines table type classification models to distinguish between wired and wireless tables, leading to more specialized tasks and higher accuracy.
 
-Table Structure Recognition (TSR) aims to extract the logical or physical structure of table images, thereby converting unstructured table images into machine-readable formats.
+🛡️ **Stable**: Does not depend on any third-party training frameworks, uses specialized ONNX models, and completely solves memory leak issues.
 
-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" width=70%>
+### Results Demonstration
+<div align="center">
+    <img src="https://github.com/RapidAI/TableStructureRec/releases/download/v0.0.0/demo_img_output.gif" alt="Demo" width="100%" height="100%">
 </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.
+### Install
+``` python {linenos=table}
+pip install wired_table_rec lineless_table_rec table_cls
+```
+
+### Quick Start
+``` python {linenos=table}
+import os
+
+from lineless_table_rec import LinelessTableRecognition
+from lineless_table_rec.utils_table_recover import format_html, plot_rec_box_with_logic_info, plot_rec_box
+from table_cls import TableCls
+from wired_table_rec import WiredTableRecognition
+
+lineless_engine = LinelessTableRecognition()
+wired_engine = WiredTableRecognition()
+table_cls = TableCls()
+img_path = f'images/img14.jpg'
+
+cls,elasp = table_cls(img_path)
+if cls == 'wired':
+    table_engine = wired_engine
+else:
+    table_engine = lineless_engine
+html, elasp, polygons, logic_points, ocr_res = table_engine(img_path)
+print(f"elasp: {elasp}")
+
+# output_dir = f'outputs'
+# complete_html = format_html(html)
+# os.makedirs(os.path.dirname(f"{output_dir}/table.html"), exist_ok=True)
+# with open(f"{output_dir}/table.html", "w", encoding="utf-8") as file:
+#     file.write(complete_html)
+# # 可视化表格识别框 + 逻辑行列信息
+# plot_rec_box_with_logic_info(
+#     img_path, f"{output_dir}/table_rec_box.jpg", logic_points, polygons
+# )
+# # 可视化 ocr 识别框
+# plot_rec_box(img_path, f"{output_dir}/ocr_box.jpg", ocr_res)
+```
 
 ### Acknowledgements
 
@@ -50,6 +82,10 @@ 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)
 
+[Qanything-RAG](https://github.com/netease-youdao/QAnything)
+
+llaipython (WeChat, commercial support for table extraction) provides high-precision wired table models.
+
 ### Contributing
 
 Pull requests are welcome. For major changes, please open an issue first

docs/README_zh.md

@@ -16,31 +16,61 @@
 
 ### 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。
+💖该仓库是用来对文档中表格做结构化识别的推理库，包括来自paddle的表格识别模型，
+阿里读光有线和无线表格识别模型，其他人贡献的有线表格模型，网易Qanything内置表格分类模型等。
 
-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直接使用。
+#### 特点
+⚡  **快**  采用ONNXRuntime作为推理引擎，cpu下单图推理1-7s
 
-该仓库会持续关注表格识别这一领域，集成最新最好用的表格识别算法，争取打造最具有落地价值的表格识别工具库。
+🎯 **准**: 结合表格类型分类模型，区分有线表格，无线表格，任务更细分，精度更高
 
-欢迎大家持续关注。
+🛡️ **稳**: 不依赖任何第三方训练框架，采用onnx专项小模型, 彻底解决了内存泄露问题
 
-### 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" width=70%>
+### 效果展示
+<div align="center">
+    <img src="https://github.com/RapidAI/TableStructureRec/releases/download/v0.0.0/demo_img_output.gif" alt="Demo" width="100%" height="100%">
 </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/)
+### 安装
+``` python {linenos=table}
+pip install wired_table_rec lineless_table_rec table_cls
+```
+
+### 快速使用
+``` python {linenos=table}
+import os
+
+from lineless_table_rec import LinelessTableRecognition
+from lineless_table_rec.utils_table_recover import format_html, plot_rec_box_with_logic_info, plot_rec_box
+from table_cls import TableCls
+from wired_table_rec import WiredTableRecognition
+
+lineless_engine = LinelessTableRecognition()
+wired_engine = WiredTableRecognition()
+table_cls = TableCls()
+img_path = f'images/img14.jpg'
+
+cls,elasp = table_cls(img_path)
+if cls == 'wired':
+    table_engine = wired_engine
+else:
+    table_engine = lineless_engine
+html, elasp, polygons, logic_points, ocr_res = table_engine(img_path)
+print(f"elasp: {elasp}")
+
+# output_dir = f'outputs'
+# complete_html = format_html(html)
+# os.makedirs(os.path.dirname(f"{output_dir}/table.html"), exist_ok=True)
+# with open(f"{output_dir}/table.html", "w", encoding="utf-8") as file:
+#     file.write(complete_html)
+# # 可视化表格识别框 + 逻辑行列信息
+# plot_rec_box_with_logic_info(
+#     img_path, f"{output_dir}/table_rec_box.jpg", logic_points, polygons
+# )
+# # 可视化 ocr 识别框
+# plot_rec_box(img_path, f"{output_dir}/ocr_box.jpg", ocr_res)
+```
 
 ### 致谢
 
@@ -50,6 +80,10 @@
 
 [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)
 
+[Qanything-RAG](https://github.com/netease-youdao/QAnything)
+
+llaipython(微信，商业化支持表格提取) 提供高精度有线表格模型。
+
 ### 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。