fix table link (#3670)

* fix table link

* fix en doc

README_en.md

@@ -533,7 +533,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 
 ### 🛠️ Installation
 
-> ❗Before installing PaddleX, please ensure you have a basic **Python runtime environment** (Note: Currently supports running under Python 3.8 to Python 3.10, with more Python versions under adaptation). The PaddlePaddle version required by PaddleX
+> ❗Before installing PaddleX, please ensure you have a basic **Python runtime environment** (Note: Currently supports running under Python 3.8 to Python 3.12, with more Python versions under adaptation). The PaddlePaddle version required by PaddleX
 
 * **Installing PaddlePaddle**
 

docs/installation/installation.en.md

@@ -3,7 +3,7 @@ comments: true
 ---
 
 # PaddleX Local Installation Tutorial
-> ❗Before installing PaddleX, please ensure you have a basic <b>Python environment</b> (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).
+> ❗Before installing PaddleX, please ensure you have a basic <b>Python environment</b> (Note: Currently supports Python 3.8 to Python 3.12, with more Python versions being adapted).
 ## 1. Quick Installation
 Welcome to PaddleX, Baidu's low-code development tool for AI. Before we dive into the local installation process, please clarify your development needs and choose the appropriate installation mode.
 

docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -189,7 +189,7 @@ comments: true
                     <li>版面检测模型： PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志、合同、书本、试卷和研报等常见的 500 张文档类型图片。</li>
                     <li>表格版面检测模型：PaddleOCR 自建的版面表格区域检测数据集，包含中英文 7835 张带有表格的论文文档类型图片。</li>
                     <li>3类版面检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 1154 张文档类型图片。</li>
-                    <li>5类英文文档区域检测模型： <a href="https://developer.ibm.com/exchanges/data/all/publaynet" target="_blank">PubLayNet</a> 的评估数据集，包含英文文档的 11245 张文图片。</li>
+                    <li>5类英文文档区域检测模型： <a href="https://developer.ibm.com/exchanges/data/all/publaynet" target="_blank">PubLayNet</a> 的评估数据集，包含英文文档的 11245 张图片。</li>
                     <li>17类区域检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 892 张文档类型图片。</li>
                  </ul>
               </li>

docs/module_usage/tutorials/time_series_modules/time_series_classification.en.md

@@ -526,6 +526,6 @@ The time series prediction module can be integrated into PaddleX pipelines such
 
 2. <b>Module Integration</b>
 
-The weights you produce can be directly integrated into the time series classification module. Refer to the Python example code in [Quick Integration](#iii-quick-integration) (Note: This section header is in Chinese and should be translated or removed for consistency), simply replace the model with the path to your trained model.
+The weights you produce can be directly integrated into the time series classification module. Refer to the Python example code in [Quick Integration](#iii-quick-integration), simply replace the model with the path to your trained model.
 
 You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md

@@ -594,7 +594,7 @@ The ultra-lightweight cyrillic alphabet recognition model trained based on the P
                          <li>Layout Region Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 500 images of common document types such as Chinese and English papers, magazines, contracts, books, exams, and research reports.</li>
                          <li>Table Layout Detection Model: A self-built layout table region detection dataset by PaddleOCR, with 7,835 images of Chinese and English paper document types containing tables.</li>
                          <li>3-Class Layout Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 1,154 images of common document types such as Chinese and English papers, magazines, and research reports.</li>
-                         <li>5-Class English Document Region Detection Model: The evaluation dataset of <a href="https://developer.ibm.com/exchanges/data/all/publaynet">PubLayNet</a>, containing 11,245 images of English documents. (Note: The link may not be accessible due to network issues or link validity. Please check the link and try again if necessary.)</li>
+                         <li>5-Class English Document Region Detection Model: The evaluation dataset of <a href="https://developer.ibm.com/exchanges/data/all/publaynet">PubLayNet</a>, containing 11,245 images of English documents. </li>
                          <li>17-Class Region Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 892 images of common document types such as Chinese and English papers, magazines, and research reports.</li>
                          <li>Table Structure Recognition Model: A self-built high-difficulty Chinese table recognition dataset by PaddleX.</li>
                          <li>Table Cell Detection Model: A self-built evaluation dataset by PaddleX.</li>
@@ -657,7 +657,7 @@ Online experience is not supported at the moment.
 Before using the General Table Recognition v2 Pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md).
 
 ### 2.3 Command Line Experience
-You can quickly experience the table recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg) (Note: The link may not be accessible due to network issues or link validity. Please check the link and try again if necessary.) and replace `--input` with the local path for prediction.
+You can quickly experience the table recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg)  and replace `--input` with the local path for prediction.
 
 ```bash
 paddlex --pipeline table_recognition_v2 \

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md

@@ -609,7 +609,7 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
                   <li>版面区域检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志、合同、书本、试卷和研报等常见的 500 张文档类型图片。</li>
                   <li>表格版面检测模型：PaddleOCR 自建的版面表格区域检测数据集，包含中英文 7835 张带有表格的论文文档类型图片。</li>
                   <li>3类版面检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 1154 张文档类型图片。</li>
-                  <li> 5类英文文档区域检测模型：[PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet) 的评估数据集，包含英文>文档的 11245 张文图片。</li>
+                  <li> 5类英文文档区域检测模型：<a href="https://developer.ibm.com/exchanges/data/all/publaynet">PubLayNet</a> 的评估数据集，包含英文文档的 11245 张图片。</li>
                   <li>17类区域检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 892 张文档类型图片。</li>
                   <li>表格结构识别模型：PaddleX 内部自建高难度中文表格识别数据集。</li>
                   <li>表格单元格检测模型：PaddleX 内部自建评测集。</li>

docs/practical_tutorials/document_scene_information_extraction(layout_detection)_tutorial.md

@@ -255,7 +255,7 @@ PaddleX 提供了 11 个版面区域定位模型，具体可参考 [模型列表
 </tr>
 </table>
 
-<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张文图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
+<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 * <b>17类区域检测模型，包含17个版面常见类别，分别是：段落标题、图片、文本、数字、摘要、内容、图表标题、公式、表格、表格标题、参考文献、文档标题、脚注、页眉、算法、页脚、印章</b>
 

docs/practical_tutorials/layout_detection.md

@@ -158,7 +158,7 @@ PaddleX 提供了丰富的模型产线，模型产线由一个或多个模型组
 </tr>
 </table>
 
-<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张文图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
+<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 * <b>17类区域检测模型，包含17个版面常见类别，分别是：段落标题、图片、文本、数字、摘要、内容、图表标题、公式、表格、表格标题、参考文献、文档标题、脚注、页眉、算法、页脚、印章</b>
 

docs/support_list/models_list.md

@@ -2426,7 +2426,7 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">训练模型</a></td>
 </tr>
 </tbody></table>
-<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张文图片。</b>
+<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张图片。</b>
 
 * <b>17类区域检测模型，包含17个版面常见类别，分别是：段落标题、图片、文本、数字、摘要、内容、图表标题、公式、表格、表格标题、参考文献、文档标题、脚注、页眉、算法、页脚、印章</b>
 <table>