移除doc目录下文档，保留fonts和doc_i18n两个目录

README.md

@@ -32,17 +32,16 @@ PaddleOCR 由 [PMC](https://github.com/PaddlePaddle/PaddleOCR/issues/12122) 监
 - 🔥🔥《PaddleOCR 2.9 发布，正式开源文本图像智能分析利器》，文本图像版面解析实现高精度实时预测，低代码全流程开发加速产业应用。集成文本图像矫正、版面区域检测、常规文本检测、印章文本检测、文本识别、表格识别等多功能。6条模型产线一键调用，显著降低开发成本。支持高性能推理、服务化部署和端侧部署等多种部署方式。**10月24日（周四）19：00**直播为您深度解析最新升级亮点。 [报名链接](https://www.wjx.top/vm/PExy7cM.aspx?udsid=896077)
 
 - **🔥2024.10.1 添加OCR领域低代码全流程开发能力**:
-  * 飞桨低代码开发工具PaddleX，依托于PaddleOCR的先进技术，支持了OCR领域的低代码全流程开发能力：
-     * 🎨 [**模型丰富一键调用**](https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/quick_start.html)：将文本图像智能分析、通用OCR、通用版面解析、通用表格识别、公式识别、印章文本识别涉及的**17个模型**整合为6条模型产线，通过极简的**Python API一键调用**，快速体验模型效果。此外，同一套API，也支持图像分类、目标检测、图像分割、时序预测等共计**200+模型**，形成20+单功能模块，方便开发者进行**模型组合**使用。
-     * 🚀[**提高效率降低门槛**](https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/overview.html)：提供基于**统一命令**和**图形界面**两种方式，实现模型简洁高效的使用、组合与定制。支持**高性能推理、服务化部署和端侧部署**等多种部署方式。此外，对于各种主流硬件如**英伟达GPU、昆仑芯、昇腾、寒武纪和海光**等，进行模型开发时，都可以**无缝切换**。
+    - 飞桨低代码开发工具PaddleX，依托于PaddleOCR的先进技术，支持了OCR领域的低代码全流程开发能力：
+        - 🎨 [**模型丰富一键调用**](https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/quick_start.html)：将文本图像智能分析、通用OCR、通用版面解析、通用表格识别、公式识别、印章文本识别涉及的**17个模型**整合为6条模型产线，通过极简的**Python API一键调用**，快速体验模型效果。此外，同一套API，也支持图像分类、目标检测、图像分割、时序预测等共计**200+模型**，形成20+单功能模块，方便开发者进行**模型组合**使用。
+        - 🚀[**提高效率降低门槛**](https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/overview.html)：提供基于**统一命令**和**图形界面**两种方式，实现模型简洁高效的使用、组合与定制。支持**高性能推理、服务化部署和端侧部署**等多种部署方式。此外，对于各种主流硬件如**英伟达GPU、昆仑芯、昇腾、寒武纪和海光**等，进行模型开发时，都可以**无缝切换**。
+
+    - 支持文档场景信息抽取v3[PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.md)、基于RT-DETR的[高精度版面区域检测模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md)和PicoDet的[高效率版面区域检测模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md)、高精度表格结构识别模型[SLANet_Plus](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)、文本图像矫正模型[UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md)、公式识别模型[LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition.md)、基于PP-LCNet的[文档图像方向分类模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md)
 
-  * 支持文档场景信息抽取v3[PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.md)、基于RT-DETR的[高精度版面区域检测模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md)和PicoDet的[高效率版面区域检测模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md)、高精度表格结构识别模型[SLANet_Plus](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)、文本图像矫正模型[UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md)、公式识别模型[LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition.md)、基于PP-LCNet的[文档图像方向分类模型](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md)
-
 - **🔥2024.7 添加 PaddleOCR 算法模型挑战赛冠军方案**：
     - 赛题一：OCR 端到端识别任务冠军方案——[场景文本识别算法-SVTRv2](https://paddlepaddle.github.io/PaddleOCR/latest/algorithm/text_recognition/algorithm_rec_svtrv2.html)；
     - 赛题二：通用表格识别任务冠军方案——[表格识别算法-SLANet-LCNetV2](https://paddlepaddle.github.io/PaddleOCR/latest/algorithm/table_recognition/algorithm_table_slanet.html)。
 
-
 ## 🌟 特性
 
 支持多种 OCR 相关前沿算法，在此基础上打造产业级特色模型PP-OCR、PP-Structure和PP-ChatOCR，并打通数据生产、模型训练、压缩、预测部署全流程。

README_en.md

@@ -30,13 +30,13 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC
 ## 📣 Recent updates ([more](https://paddlepaddle.github.io/PaddleOCR/latest/en/update.html))
 
 - **🔥 2024.10.18 release PaddleOCR v2.9, including**:
-  * PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
-     * 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**.
+    - PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
+        - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**.
 
-     * 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**.
+        - 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**.
+
+    - Supports [PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_en.md), [high-precision layout detection model based on RT-DETR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) and [high-efficiency layout area detection model based on PicoDet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md), [high-precision table structure recognition model](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md), text image unwarping model [UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md), formula recognition model [LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md), and [document image orientation classification model based on PP-LCNet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md).
 
-  * Supports [PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_en.md), [high-precision layout detection model based on RT-DETR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) and [high-efficiency layout area detection model based on PicoDet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md), [high-precision table structure recognition model](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md), text image unwarping model [UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md), formula recognition model [LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md), and [document image orientation classification model based on PP-LCNet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md).
-
 - **🔥2024.7 Added PaddleOCR Algorithm Model Challenge Champion Solutions**:
     - Challenge One, OCR End-to-End Recognition Task Champion Solution: [Scene Text Recognition Algorithm-SVTRv2](https://paddlepaddle.github.io/PaddleOCR/algorithm/text_recognition/algorithm_rec_svtrv2.html);
     - Challenge Two, General Table Recognition Task Champion Solution: [Table Recognition Algorithm-SLANet-LCNetV2](https://paddlepaddle.github.io/PaddleOCR/algorithm/table_recognition/algorithm_table_slanet.html).