rename to PP-StructureV3 in docs

docs/CHANGLOG.en.md

@@ -12,7 +12,7 @@ PaddleX 3.0 rc0 is fully compatible with PaddlePaddle 3.0rc0 version, adding 10+
 - <b>New pipelines</b>:
   - <b>[Document Image Preprocessing Pipeline](pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.en.md)</b>, supporting the correction of rotated and distorted document images.
   - <b>[PP-ChatOCRv4-doc Pipeline](pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.en.md)</b>, which integrates multimodal capabilities on the basis of the Document PP-ChatOCRv3-doc pipeline, enhances OCR recognition capabilities, optimizes Prompts, and ultimately improves the accuracy of document information extraction by 15 percentage points. Supports local large model OpenAI interface calls.
-  - <b>[Layout Parsing v2 Pipeline](pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.en.md)</b>, the core solution of PP-StructureV3. Based on the General Layout Parsing v1 pipeline, it optimizes layout area detection, table recognition, formula recognition, and reading order recovery capabilities, supports converting different types of document images and document PDF files into standard Markdown files, and performs strongly in document recovery capabilities in most scenarios.
+  - <b>[Layout Parsing v3 Pipeline](pipeline_usage/tutorials/ocr_pipelines/layout_parsing.en.md)</b>, the core solution of PP-StructureV3. Based on the General Layout Parsing v1 pipeline, it optimizes layout area detection, table recognition, formula recognition, and reading order recovery capabilities, supports converting different types of document images and document PDF files into standard Markdown files, and performs strongly in document recovery capabilities in most scenarios.
   - <b>[Table Recognition v2 Pipeline](pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md)</b>, adopting a multi-model series networking solution of "table classification + table structure recognition + cell detection" to achieve higher precision end-to-end table recognition.
   - <b>[Rotated Object Detection Pipeline](pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md)</b>, supporting the detection of rotated objects.
   - <b>[Human Keypoint Detection Pipeline](pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.en.md)</b>, supporting precise acquisition of human keypoint positions such as shoulders, elbows, knees, etc., for pose estimation and behavior recognition.

docs/CHANGLOG.md

@@ -11,7 +11,7 @@ PaddleX 3.0 rc0 全面适配 PaddlePaddle 3.0rc0 版本，新增10+条产线，4
 - 新增产线：
   - 新增[文档预处理产线](pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.md)，支持将矫正旋转和扭曲的文档图像。
   - 新增[文档场景信息抽取v4产线](pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.md)，在文档场景信息抽取v3产线的基础上，融合了多模态能力，增强了OCR识别能力，优化了Prompt，最终文档信息抽取的准确率提升15个百分点。支持本地大模型OpenAI接口调用。
-  - 新增[通用版面解析v2产线](pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.md)，PP-StructureV3 的核心方案。在通用版面解析v1产线的基础上，优化了版面区域检测、表格识别、公式识别、阅读顺序恢复的能力，支持将不同类型的文档图像和文档PDF文件转换为标准的Markdown文件，在大多数场景的文档恢复能力表现强劲。
+  - 新增[通用版面解析v3产线](pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md)，PP-StructureV3 的核心方案。在通用版面解析v1产线的基础上，优化了版面区域检测、表格识别、公式识别、阅读顺序恢复的能力，支持将不同类型的文档图像和文档PDF文件转换为标准的Markdown文件，在大多数场景的文档恢复能力表现强劲。
   - 新增[通用表格识别v2产线](pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md)，采用了“表格分类+表格结构识别+单元格检测”的多模型串联组网方案，实现更高精度的端到端表格识别。
   - 新增[旋转框检测产线](pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md)，支持对旋转目标进行检测。
   - 新增[人体关键点检测产线](pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.md)，支持精确获取人体的关键点位置，如肩膀、肘部、膝盖等，从而进行姿态估计和行为识别。

docs/index.en.md

@@ -526,7 +526,7 @@ Each pipeline in PaddleX corresponds to specific parameters. You can find detail
                     [ 39, ..., 726]], dtype=int16)}, 'formula_res_list': [{'input_path': None, 'page_index': None, 'rec_formula': '\\begin{array}{r l}{\\mathcal{Q}}&{=\\mathcal{K}=\\mathcal{V}=\\mathtt{F l a t t e r n}(\\mathcal{S}_{5}),}\\\\ {\\mathcal{F}_{5}}&{=\\mathtt{R e s h a p e}(\\mathtt{A I F I}(\\mathcal{Q},\\mathcal{K},\\mathcal{V})),\\qquad\\quad(1)}\\\\ {\\mathcal{O}}&{=\\mathtt{C C F F}(\\{\\mathcal{S}_{3},\\mathcal{S}_{4},\\mathcal{F}_{5}\\}),}\\end{array}', 'formula_region_id': 1, 'dt_polys': [92.092064, 563.1221, 297.57217, 607.2598]}, {'input_path': None, 'page_index': None, 'rec_formula': '\\begin{array}{r l r}{\\mathcal{U}(\\hat{\\mathcal{X}})=\\|\\mathcal{P}(\\hat{\\mathcal{X}})-\\mathcal{C}(\\hat{\\mathcal{X}})\\|,\\hat{\\mathcal{X}}\\in\\mathbb{R}^{D}}&{{}(2)}&{}\\\\ {\\mathcal{L}(\\hat{\\mathcal{X}},\\hat{\\mathcal{Y}},\\mathcal{Y})=\\mathcal{L}_{t o x}(\\hat{\\mathbf{b}},\\mathbf{b})+\\mathcal{L}_{c l s}(\\mathcal{U}(\\hat{\\mathcal{X}}),\\hat{\\mathbf{c}},\\mathbf{c})}&{{}(3)}\\end{array}', 'formula_region_id': 2, 'dt_polys': [343.82712, 551.06995, 573.45465, 589.9438]}]}}
                 ```
 
-    === "Layout Parsing v2"
+    === "Layout Parsing v3"
 
         ```bash
         paddlex --pipeline PP-StructureV3 \

docs/index.md

@@ -300,7 +300,7 @@ PaddleX的每一条产线对应特定的参数，您可以在各自的产线文
                 ...,
                 [1156, ..., 1351]], dtype=int16)}}}
             ```
-    === "通用版面解析v2"
+    === "通用版面解析v3"
 
         ```bash
         paddlex --pipeline PP-StructureV3 \
@@ -1057,7 +1057,7 @@ for res in output:
 
         ```
 
-    === "通用版面解析v2"
+    === "通用版面解析v3"
 
         ```python
         from paddlex import create_pipeline
@@ -1425,11 +1425,11 @@ for res in output:
 
     [:octicons-arrow-right-24: 教程](pipeline_usage/tutorials/ocr_pipelines/OCR.md)
 
-- **通用版面解析v2**
+- **通用版面解析v3**
 
     ---
 
-    通用版面解析v2产线在通用版面解析v1产线的基础上，强化了版面区域检测、表格识别、公式识别的能力，增加了多栏阅读顺序的恢复能力、结果转换 Markdown 文件的能力，在多种文档数据中，表现优异，可以处理较复杂的文档数据。
+    通用版面解析v3产线在通用版面解析v1产线的基础上，强化了版面区域检测、表格识别、公式识别的能力，增加了多栏阅读顺序的恢复能力、结果转换 Markdown 文件的能力，在多种文档数据中，表现优异，可以处理较复杂的文档数据。
 
     [:octicons-arrow-right-24: 教程](pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.md)
 

docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.md

@@ -429,7 +429,7 @@ python main.py -c paddlex/configs/modules/textline_orientation/PP-LCNet_x0_25_te
 
 1.<b>产线集成</b>
 
-文本行方向分类模块可以集成的PaddleX产线有[通用OCR产线](../../../pipeline_usage/tutorials/ocr_pipelines/OCR.md)、[通用版面解析产线](../../../pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md)、[通用版面解析v2产线](../../../pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.md)和[文档场景信息抽取v3产线（PP-ChatOCRv3-doc）](../../../pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.md)，只需要替换模型路径即可完成文本行方向分类模块的模型更新。
+文本行方向分类模块可以集成的PaddleX产线有[通用OCR产线](../../../pipeline_usage/tutorials/ocr_pipelines/OCR.md)、[通用版面解析产线](../../../pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md)、[通用版面解析v3产线](../../../pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md)和[文档场景信息抽取v3产线（PP-ChatOCRv3-doc）](../../../pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.md)，只需要替换模型路径即可完成文本行方向分类模块的模型更新。
 
 2.<b>模块集成</b>
 

docs/pipeline_usage/pipeline_develop_guide.en.md

@@ -269,8 +269,8 @@ Choose the appropriate deployment method for your model pipeline based on your n
 <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">Layout Parsing Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
-<td>Layout Parsing v2</td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html">Layout Parsing v2 Pipeline Usage Tutorial</a></td>
+<td>Layout Parsing v3</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html">Layout Parsing v3 Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Formula Recognition</td>

docs/pipeline_usage/pipeline_develop_guide.md

@@ -271,8 +271,8 @@ Pipeline:
 <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">通用版面解析产线使用教程</a></td>
 </tr>
 <tr>
-<td>通用版面解析v2</td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html">通用版面解析v2产线使用教程</a></td>
+<td>通用版面解析v3</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html">通用版面解析v3产线使用教程</a></td>
 </tr>
 <tr>
 <td>公式识别</td>