[Docs] refine table related docs (#3295)

* add table docs

* fix bugs

* refine docs

* refine docs

* refine codes

docs/module_usage/tutorials/ocr_modules/table_classification.en.md

@@ -13,20 +13,20 @@ The table classification module is a key component of a computer vision system,
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>Top1 Acc(%)</th>
-<th>GPU Inference Time (ms)</th>
-<th>CPU Inference Time (ms)</th>
+<th>GPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>Model Storage Size (M)</th>
 </tr>
 <tr>
 <td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/CLIP_vit_base_patch16_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">Training Model</a></td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
 <td>6.6M</td>
 </tr>
 </table>
 
-<p><b>Note: The above accuracy metrics are measured from the internal table classification dataset built by PaddleX. All models' GPU inference time is based on an NVIDIA Tesla T4 machine, with a precision type of FP32. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and a precision type of FP32.</b></p></details>
+<p><b>Note: The above accuracy metrics are measured from the internal table classification dataset built by PaddleX. All models' GPU inference time is based on an NVIDIA Tesla T4 machine, with a precision type of FP32. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and a precision type of FP32.</b></p>
 
 ## III. Quick Integration
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, please refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
@@ -44,12 +44,15 @@ for res in output:
 
 After running the code, the result obtained is:
 
-```json
+```
 {'res': {'input_path': 'table_recognition.jpg', 'class_ids': array([0, 1], dtype=int32), 'scores': array([0.84421, 0.15579], dtype=float32), 'label_names': ['wired_table', 'wireless_table']}}
 ```
 
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/table_classification/01.jpg">
+
 The meanings of the parameters in the running results are as follows:
 - `input_path`: Indicates the path of the input image.
+- `page_index`：If the input is a PDF file, this indicates the current page number of the PDF. Otherwise, it is `null`
 - `class_ids`: Indicates the class ID of the prediction result.
 - `scores`: Indicates the confidence of the prediction result.
 - `label_names`: Indicates the class name of the prediction result.
@@ -100,7 +103,7 @@ The descriptions of the related methods and parameters are as follows:
 <tr>
 <td><code>input</code></td>
 <td>Data to be predicted, supporting multiple input types</td>
-<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td><code>Python Var</code>/<code>str</code>/<code>list</code></td>
 <td>
 <ul>
   <li><b>Python variable</b>, such as image data represented by <code>numpy.ndarray</code></li>
@@ -186,8 +189,12 @@ The descriptions of the related methods and parameters are as follows:
 </tr>
 </thead>
 <tr>
-<td rowspan = "1"><code>json</code></td>
-<td rowspan = "1">Get the prediction result in <code>json</code> format</td>
+<td rowspan="1"><code>json</code></td>
+<td rowspan="1">Get the prediction result in <code>json</code> format</td>
+</tr>
+<tr>
+<td rowspan="1"><code>img</code></td>
+<td rowspan="1">Get the visualization image in <code>dict</code> format</td>
 </tr>
 </table>
 
@@ -271,7 +278,7 @@ After executing the above command, PaddleX will verify the dataset and collect b
 <li><code>attributes.val_sample_paths</code>: A list of relative paths for the visualization images of the validation samples in this dataset;</li>
 </ul>
 <p>In addition, the dataset verification has analyzed the distribution of sample counts for all classes in the dataset and generated a histogram (histogram.png):</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/modules/table_classification/01.png"></p></details></url> 
+<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/modules/table_classification/02.png"></p></details>
 
 #### 4.1.3 Dataset Format Conversion/Dataset Splitting (Optional)
 After you complete the data verification, you can convert the dataset format by <b>modifying the configuration file</b> or <b>adding hyperparameters</b>. You can also re-split the training/validation ratio of the dataset.

docs/module_usage/tutorials/ocr_modules/table_classification.md

@@ -14,20 +14,20 @@ comments: true
 <tr>
 <th>模型</th><th>模型下载链接</th>
 <th>Top1 Acc(%)</th>
-<th>GPU推理耗时 (ms)</th>
-<th>CPU推理耗时 (ms)</th>
+<th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
+<th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小 (M)</th>
 </tr>
 <tr>
 <td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/CLIP_vit_base_patch16_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
 <td>6.6M</td>
 </tr>
 </table>
 
-<p><b>注：以上精度指标测量自 PaddleX 内部自建表格分类数据集。所有模型 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></p></details>
+<p><b>注：以上精度指标测量自 PaddleX 内部自建评测数据集。所有模型 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></p>
 
 ## 三、快速集成
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)。
@@ -44,15 +44,20 @@ for res in output:
 ```
 
 运行后，得到的结果为：
-```json
-{'res': {'input_path': 'table_recognition.jpg', 'class_ids': array([0, 1], dtype=int32), 'scores': array([0.84421, 0.15579], dtype=float32), 'label_names': ['wired_table', 'wireless_table']}}
+```
+{"res": {"input_path": "table_recognition.jpg", "page_index": null, "class_ids": array([0, 1], dtype=int32), "scores": array([0.84421, 0.15579], dtype=float32), "label_names": ["wired_table", "wireless_table"]}}
 ```
 
 运行结果参数含义如下：
-- `input_path`：表示输入图片的路径。
-- `class_ids`：表示预测结果的类别id。
-- `scores`：表示预测结果的置信度。
-- `label_names`：表示预测结果的类别名。
+- `input_path`：表示输入图片的路径
+- `page_index`：如果输入是PDF文件，则表示当前是PDF的第几页，否则为 `null`
+- `class_ids`：表示预测结果的类别id
+- `scores`：表示预测结果的置信度
+- `label_names`：表示预测结果的类别名
+
+可视化图像如下：
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/table_classification/01.jpg">
 
 相关方法、参数等说明如下：
 
@@ -100,7 +105,7 @@ for res in output:
 <tr>
 <td><code>input</code></td>
 <td>待预测数据，支持多种输入类型</td>
-<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td><code>Python Var</code>/<code>str</code>/<code>list</code></td>
 <td>
 <ul>
   <li><b>Python变量</b>，如<code>numpy.ndarray</code>表示的图像数据</li>
@@ -189,9 +194,12 @@ for res in output:
 <td rowspan = "1"><code>json</code></td>
 <td rowspan = "1">获取预测的<code>json</code>格式的结果</td>
 </tr>
+<tr>
+<td rowspan = "1"><code>img</code></td>
+<td rowspan = "1">获取可视化图像</td>
+</tr>
 </table>
 
-
 关于更多 PaddleX 的单模型推理的 API 的使用方法，可以参考[PaddleX单模型Python脚本使用说明](../../instructions/model_python_API.md)。
 
 ## 四、二次开发
@@ -271,7 +279,7 @@ python main.py -c paddlex/configs/modules/table_classification/PP-LCNet_x1_0_tab
 <li><code>attributes.val_sample_paths</code>：该数据集验证集样本可视化图片相对路径列表；</li>
 </ul>
 <p>另外，数据集校验还对数据集中所有类别的样本数量分布情况进行了分析，并绘制了分布直方图（histogram.png）：</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/modules/table_classification/01.png"></p></details>
+<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/modules/table_classification/02.png"></p></details>
 
 #### 4.1.3 数据集格式转换/数据集划分（可选）
 在您完成数据校验之后，可以通过<b>修改配置文件</b>或是<b>追加超参数</b>的方式对数据集的格式进行转换，也可以对数据集的训练/验证比例进行重新划分。

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.en.md

@@ -37,7 +37,21 @@ Table structure recognition is a crucial component in table recognition systems,
 SLANet_plus is an enhanced version of SLANet, a table structure recognition model developed by Baidu PaddlePaddle's Vision Team. Compared to SLANet, SLANet_plus significantly improves its recognition capabilities for wireless and complex tables, while reducing the model's sensitivity to the accuracy of table localization. Even when there are offsets in table localization, it can still perform relatively accurate recognition.
 </td>
 </tr>
+<tr>
+<td>SLANeXt_wired</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/SLANeXt_wired_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wired_pretrained.pdparams">Trained Model</a></td>
+<td rowspan="2">69.65</td>
+<td rowspan="2"></td>
+<td rowspan="2"></td>
+<td rowspan="2">351M</td>
+<td rowspan="2">SLANeXt series is a new generation of form structure recognition model developed by Baidu PaddlePaddle's Vision Team. Compared with SLANet and SLANet_plus, SLANeXt focuses on the recognition of form structure, and special weights are trained for the recognition of wired and wireless forms, which significantly improves the recognition ability of all types of forms, especially the recognition ability of wired forms is greatly improved.</td>
+</tr>
+<tr>
+<td>SLANeXt_wireless</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/SLANeXt_wireless_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wireless_pretrained.pdparams">Trained Model</a></td>
+</tr>
 </table>
+
 <b>Note: The above accuracy metrics are evaluated on a self-built English table recognition dataset by PaddleX. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
 
@@ -57,13 +71,14 @@ for res in output:
 
 <details><summary>👉 <b>After running, the result is: (Click to expand)</b></summary>
 
-```json
-{'res': {'input_path': 'table_recognition.jpg', 'bbox': [array([ 42,   2, 390,   2, 388,  27,  40,  26]), array([11, 35, 89, 35, 87, 63, 11, 63]), array([113,  34, 192,  34, 186,  64, 109,  64]), array([219,  33, 399,  33, 393,  62, 212,  62]), array([413,  33, 544,  33, 544,  64, 407,  64]), array([12, 67, 98, 68, 96, 93, 12, 93]), array([115,  66, 205,  66, 200,  91, 111,  91]), array([234,  65, 390,  65, 385,  92, 227,  92]), array([414,  66, 537,  67, 537,  95, 409,  95]), array([  7,  97, 106,  97, 104, 128,   7, 128]), array([113,  96, 206,  95, 201, 127, 109, 127]), array([236,  96, 386,  96, 381, 128, 230, 128]), array([413,  96, 534,  95, 533, 127, 408, 127])], 'structure': ['<html>', '<body>', '<table>', '<tr>', '<td', '="" ',="" colspan="4">', '', '</td',></tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</table>', '</body>', '</html>'], 'structure_score': 0.99948007}}
+```
+{'res': {'input_path': 'table_recognition.jpg', 'page_index': null, 'bbox': [array([ 42,   2, 390,   2, 388,  27,  40,  26]), array([11, 35, 89, 35, 87, 63, 11, 63]), array([113,  34, 192,  34, 186,  64, 109,  64]), array([219,  33, 399,  33, 393,  62, 212,  62]), array([413,  33, 544,  33, 544,  64, 407,  64]), array([12, 67, 98, 68, 96, 93, 12, 93]), array([115,  66, 205,  66, 200,  91, 111,  91]), array([234,  65, 390,  65, 385,  92, 227,  92]), array([414,  66, 537,  67, 537,  95, 409,  95]), array([  7,  97, 106,  97, 104, 128,   7, 128]), array([113,  96, 206,  95, 201, 127, 109, 127]), array([236,  96, 386,  96, 381, 128, 230, 128]), array([413,  96, 534,  95, 533, 127, 408, 127])], 'structure': ['<html>', '<body>', '<table>', '<tr>', '<td', ' colspan="4"', '>', '</td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</table>', '</body>', '</html>'], 'structure_score': 0.99948007}}
 ```
 
 Parameter meanings are as follows:
 <ul>
 <li><code>input_path</code>: The path of the input image to be predicted</li>
+<li><code>page_index</code>：If the input is a PDF file, this indicates the current page number of the PDF. Otherwise, it is `null`</li>
 <li><code>boxes</code>: Predicted table cell information, a list composed of several predicted table cell coordinates. Note that the table cell predictions from the SLANeXt series models are invalid</li>
 <li><code>structure</code>: Predicted table structure in HTML expressions, a list composed of several predicted HTML keywords in order</li>
 <li><code>structure_score</code>: Confidence score of the predicted table structure</li>
@@ -116,7 +131,7 @@ Relevant methods, parameters, and explanations are as follows:
 <tr>
 <td><code>input</code></td>
 <td>Data to be predicted, supporting multiple input types</td>
-<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td><code>Python Var</code>/<code>str</code>/<code>list</code></td>
 <td>
 <ul>
 <li><b>Python Variable</b>, such as image data represented by <code>numpy.ndarray</code></li>
@@ -192,7 +207,7 @@ Relevant methods, parameters, and explanations are as follows:
 </tr>
 </table>
 
-* In addition, it also supports obtaining a visualization image with results through attributes, as follows:
+* In addition, it also supports obtaining results through attributes, as follows:
 
 <table>
 <thead>
@@ -241,29 +256,19 @@ After executing the above command, PaddleX will validate the dataset and summari
   "attributes": {
     "train_samples": 2000,
     "train_sample_paths": [
-      "../dataset/table_rec_dataset_examples/images/border_right_7384_X9UFEPKVMLALY7DDB11A.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_top_13708_VE2DGBD4DCQU2ITLBTEA.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_top_6490_14Z6ZN6G52GG4XA0K4XU.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_top_14236_DG96EX0EDKIIDK8P6ENG.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_19648_SV8B7X34RTYRAT2T5CPI.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_bottom_7186_HODBC25HISMCSVKY0HJ9.jpg",
-      "../dataset/table_rec_dataset_examples/images/head_border_bottom_5773_4K4H9OVK9X9YVHE4Y1BQ.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_7760_8C62CCH5T57QUGE0NTHZ.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_bottom_15707_B1YVOU3X4NHHB6TL269O.jpg",
-      "../dataset/table_rec_dataset_examples/images/no_border_5223_HLG406UK35UD5EUYC2AV.jpg"
+      "check_dataset/demo_img/border_right_7384_X9UFEPKVMLALY7DDB11A.jpg",
+      "check_dataset/demo_img/border_top_13708_VE2DGBD4DCQU2ITLBTEA.jpg",
+      "check_dataset/demo_img/border_top_6490_14Z6ZN6G52GG4XA0K4XU.jpg",
+      "check_dataset/demo_img/border_top_14236_DG96EX0EDKIIDK8P6ENG.jpg",
+      "check_dataset/demo_img/border_19648_SV8B7X34RTYRAT2T5CPI.jpg",
     ],
     "val_samples": 100,
     "val_sample_paths": [
-      "../dataset/table_rec_dataset_examples/images/border_2945_L7MSRHBZRW6Y347G39O6.jpg",
-      "../dataset/table_rec_dataset_examples/images/head_border_bottom_4825_LH9WI6X104CP3VFXPSON.jpg",
-      "../dataset/table_rec_dataset_examples/images/head_border_bottom_16837_79KHWU9WDM9ZQHNBGQAL.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_bottom_10107_9ENLLC29SQ6XI8WZY53E.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_top_16668_JIS0YFDZKTKETZIEKCKX.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_18653_J9SSKHLFTRJD4J8W17OW.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_bottom_8396_VJ3QJ3I0DP63P4JR77FE.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_9017_K2V7QBWSU2BA4R3AJSO7.jpg",
-      "../dataset/table_rec_dataset_examples/images/border_top_19494_SDFMWP92NOB2OT7109FI.jpg",
-      "../dataset/table_rec_dataset_examples/images/no_border_288_6LK683JUCMOQ38V5BV29.jpg"
+      "check_dataset/demo_img/border_18653_J9SSKHLFTRJD4J8W17OW.jpg",
+      "check_dataset/demo_img/border_bottom_8396_VJ3QJ3I0DP63P4JR77FE.jpg",
+      "check_dataset/demo_img/border_9017_K2V7QBWSU2BA4R3AJSO7.jpg",
+      "check_dataset/demo_img/border_top_19494_SDFMWP92NOB2OT7109FI.jpg",
+      "check_dataset/demo_img/no_border_288_6LK683JUCMOQ38V5BV29.jpg"
     ]
   },
   "analysis": {},

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md

@@ -67,11 +67,12 @@ for res in output:
 <details><summary>👉 <b>运行后，得到的结果为：（点击展开）</b></summary>
 
 ```json
-{'res': {'input_path': 'table_recognition.jpg', 'bbox': [array([ 42,   2, 390,   2, 388,  27,  40,  26]), array([11, 35, 89, 35, 87, 63, 11, 63]), array([113,  34, 192,  34, 186,  64, 109,  64]), array([219,  33, 399,  33, 393,  62, 212,  62]), array([413,  33, 544,  33, 544,  64, 407,  64]), array([12, 67, 98, 68, 96, 93, 12, 93]), array([115,  66, 205,  66, 200,  91, 111,  91]), array([234,  65, 390,  65, 385,  92, 227,  92]), array([414,  66, 537,  67, 537,  95, 409,  95]), array([  7,  97, 106,  97, 104, 128,   7, 128]), array([113,  96, 206,  95, 201, 127, 109, 127]), array([236,  96, 386,  96, 381, 128, 230, 128]), array([413,  96, 534,  95, 533, 127, 408, 127])], 'structure': ['<html>', '<body>', '<table>', '<tr>', '<td', '="" ',="" colspan="4">', '', '</td',></tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</table>', '</body>', '</html>'], 'structure_score': 0.99948007}}
+{'res': {'input_path': 'table_recognition.jpg', 'page_index': null, 'bbox': [array([ 42,   2, 390,   2, 388,  27,  40,  26]), array([11, 35, 89, 35, 87, 63, 11, 63]), array([113,  34, 192,  34, 186,  64, 109,  64]), array([219,  33, 399,  33, 393,  62, 212,  62]), array([413,  33, 544,  33, 544,  64, 407,  64]), array([12, 67, 98, 68, 96, 93, 12, 93]), array([115,  66, 205,  66, 200,  91, 111,  91]), array([234,  65, 390,  65, 385,  92, 227,  92]), array([414,  66, 537,  67, 537,  95, 409,  95]), array([  7,  97, 106,  97, 104, 128,   7, 128]), array([113,  96, 206,  95, 201, 127, 109, 127]), array([236,  96, 386,  96, 381, 128, 230, 128]), array([413,  96, 534,  95, 533, 127, 408, 127])], 'structure': ['<html>', '<body>', '<table>', '<tr>', '<td', ' colspan="4"', '>', '</td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</table>', '</body>', '</html>'], 'structure_score': 0.99948007}}
 ```
 
 参数含义如下：
-- `input_path`：输入的待预测图像的路径
+- `input_path`：输入的待预测表格图像的路径
+- `page_index`：如果输入是PDF文件，则表示当前是PDF的第几页，否则为 `null`
 - `boxes`：预测的表格单元格信息，一个列表，由预测的若干表格单元格坐标组成。特别地， SLANeXt 系列模型预测的表格单元格无效
 - `structure`：预测的表格结构Html表达式，一个列表，由预测的若干Html关键字按顺序组成
 - `structure_score`：预测表格结构的置信度
@@ -131,7 +132,7 @@ for res in output:
 <tr>
 <td><code>input</code></td>
 <td>待预测数据，支持多种输入类型</td>
-<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td><code>Python Var</code>/<code>str</code>/<code>list</code></td>
 <td>
 <ul>
   <li><b>Python变量</b>，如<code>numpy.ndarray</code>表示的图像数据</li>
@@ -207,7 +208,7 @@ for res in output:
 </tr>
 </table>
 
-* 此外，也支持通过属性获取带结果的可视化图像，具体如下：
+* 此外，也支持通过属性获取结果，具体如下：
 
 <table>
 <thead>
@@ -256,29 +257,19 @@ python main.py -c paddlex/configs/modules/table_structure_recognition/SLANet.yam
   "attributes": {
     "train_samples": 2000,
     "train_sample_paths": [
-      "check_dataset\/demo_img\/border_right_7384_X9UFEPKVMLALY7DDB11A.jpg",
-      "check_dataset\/demo_img\/border_top_13708_VE2DGBD4DCQU2ITLBTEA.jpg",
-      "check_dataset\/demo_img\/border_top_6490_14Z6ZN6G52GG4XA0K4XU.jpg",
-      "check_dataset\/demo_img\/border_top_14236_DG96EX0EDKIIDK8P6ENG.jpg",
-      "check_dataset\/demo_img\/border_19648_SV8B7X34RTYRAT2T5CPI.jpg",
-      "check_dataset\/demo_img\/border_bottom_7186_HODBC25HISMCSVKY0HJ9.jpg",
-      "check_dataset\/demo_img\/head_border_bottom_5773_4K4H9OVK9X9YVHE4Y1BQ.jpg",
-      "check_dataset\/demo_img\/border_7760_8C62CCH5T57QUGE0NTHZ.jpg",
-      "check_dataset\/demo_img\/border_bottom_15707_B1YVOU3X4NHHB6TL269O.jpg",
-      "check_dataset\/demo_img\/no_border_5223_HLG406UK35UD5EUYC2AV.jpg"
+      "check_dataset/demo_img/border_right_7384_X9UFEPKVMLALY7DDB11A.jpg",
+      "check_dataset/demo_img/border_top_13708_VE2DGBD4DCQU2ITLBTEA.jpg",
+      "check_dataset/demo_img/border_top_6490_14Z6ZN6G52GG4XA0K4XU.jpg",
+      "check_dataset/demo_img/border_top_14236_DG96EX0EDKIIDK8P6ENG.jpg",
+      "check_dataset/demo_img/border_19648_SV8B7X34RTYRAT2T5CPI.jpg",
     ],
     "val_samples": 100,
     "val_sample_paths": [
-      "check_dataset\/demo_img\/border_2945_L7MSRHBZRW6Y347G39O6.jpg",
-      "check_dataset\/demo_img\/head_border_bottom_4825_LH9WI6X104CP3VFXPSON.jpg",
-      "check_dataset\/demo_img\/head_border_bottom_16837_79KHWU9WDM9ZQHNBGQAL.jpg",
-      "check_dataset\/demo_img\/border_bottom_10107_9ENLLC29SQ6XI8WZY53E.jpg",
-      "check_dataset\/demo_img\/border_top_16668_JIS0YFDZKTKETZIEKCKX.jpg",
-      "check_dataset\/demo_img\/border_18653_J9SSKHLFTRJD4J8W17OW.jpg",
-      "check_dataset\/demo_img\/border_bottom_8396_VJ3QJ3I0DP63P4JR77FE.jpg",
-      "check_dataset\/demo_img\/border_9017_K2V7QBWSU2BA4R3AJSO7.jpg",
-      "check_dataset\/demo_img\/border_top_19494_SDFMWP92NOB2OT7109FI.jpg",
-      "check_dataset\/demo_img\/no_border_288_6LK683JUCMOQ38V5BV29.jpg"
+      "check_dataset/demo_img/border_18653_J9SSKHLFTRJD4J8W17OW.jpg",
+      "check_dataset/demo_img/border_bottom_8396_VJ3QJ3I0DP63P4JR77FE.jpg",
+      "check_dataset/demo_img/border_9017_K2V7QBWSU2BA4R3AJSO7.jpg",
+      "check_dataset/demo_img/border_top_19494_SDFMWP92NOB2OT7109FI.jpg",
+      "check_dataset/demo_img/no_border_288_6LK683JUCMOQ38V5BV29.jpg"
     ]
   },
   "analysis": {},

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

@@ -242,6 +242,12 @@ In the above Python script, the following steps are executed:
 <td><code>gpu</code></td>
 </tr>
 <tr>
+<td><code>config</code></td>
+<td>Specific configuration information for the production line (if set simultaneously with <code>pipeline</code>, it has higher priority than <code>pipeline</code>, and the production line name must be consistent with <code>pipeline</code>).</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
+</tr>
+<tr>
 <td><code>use_hpip</code></td>
 <td>Whether to enable high-performance inference, only available if the production line supports it.</td>
 <td><code>bool</code></td>

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.md

@@ -288,7 +288,7 @@ paddlex --pipeline table_recognition \
         --device gpu:0
 ```
 
-相关的参数说明可以参考[2.2.2 Python脚本方式集成](#222-python脚本方式集成)中的参数说明。
+相关的参数说明可以参考[2.2 Python脚本方式](#22-python脚本方式集成)中的参数说明。
 
 运行后，会将结果打印到终端上，结果如下：
 
@@ -426,7 +426,7 @@ paddlex --pipeline table_recognition \
        [508, 126],
        [451, 126]], dtype=int16)], 'rec_texts': ['CRuncover', 'Dres', '连续工作3', '取出来放在网上，没想', '江、江等八大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': [0.9943075180053711, 0.9951075315475464, 0.9907732009887695, 0.9975494146347046, 0.9974043369293213, 0.9983242750167847, 0.991967499256134, 0.9898287653923035, 0.9961177110671997, 0.9975040555000305, 0.9986456632614136, 0.9987970590591431], 'rec_boxes': [array([232,   0, 318,  24], dtype=int16), array([32, 38, 67, 55], dtype=int16), array([119,  34, 196,  57], dtype=int16), array([222,  29, 396,  60], dtype=int16), array([419,  30, 542,  61], dtype=int16), array([29, 71, 72, 92], dtype=int16), array([287,  72, 329,  93], dtype=int16), array([456,  68, 501,  94], dtype=int16), array([  8, 101,  89, 130], dtype=int16), array([139, 105, 172, 126], dtype=int16), array([274, 101, 340, 130], dtype=int16), array([451, 103, 508, 126], dtype=int16)]}}]}}
 ```
-运行结果参数说明可以参考[2.2.2 Python脚本方式集成](#222-python脚本方式集成)中的结果解释。
+运行结果参数说明可以参考[2.2 Python脚本方式](#22-python脚本方式集成)中的结果解释。
 
 可视化结果保存在`save_path`下，其中表格识别的可视化结果如下：
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition/03.png"/>
@@ -438,7 +438,7 @@ paddlex --pipeline table_recognition \
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="table_recognition")
+pipeline = create_pipeline(pipeline="table_recognition")
 
 output = pipeline.predict(
     input="table_recognition.jpg",
@@ -475,6 +475,12 @@ for res in output:
 <td><code>None</code></td>
 </tr>
 <tr>
+<td><code>config</code></td>
+<td>产线具体的配置信息（如果和<code>pipeline</code>同时设置，优先级高于<code>pipeline</code>，且要求产线名和<code>pipeline</code>一致）。</td>
+<td><code>dict[str, Any]</code></td>
+<td><code>None</code></td>
+</tr>
+<tr>
 <td><code>device</code></td>
 <td>产线推理设备。支持指定GPU具体卡号，如“gpu:0”，其他硬件具体卡号，如“npu:0”，CPU如“cpu”。</td>
 <td><code>str</code></td>
@@ -765,7 +771,7 @@ paddlex --get_pipeline_config table_recognition --save_path ./my_path
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="./my_path/table_recognition.yaml")
+pipeline = create_pipeline(pipeline="./my_path/table_recognition.yaml")
 
 output = pipeline.predict(
     input="table_recognition.jpg",

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

@@ -10,8 +10,12 @@ Table recognition is a technology that automatically identifies and extracts tab
 The General Table Recognition Pipeline v2 is designed to solve table recognition tasks by identifying tables in images and outputting them in HTML format. Unlike the General Table Recognition Pipeline, this pipeline introduces two additional modules: table classification and table cell detection, which are linked with the table structure recognition module to complete the table recognition task. This pipeline can achieve accurate table predictions and is applicable in various fields such as general, manufacturing, finance, and transportation. It also provides flexible service deployment options, supporting multiple programming languages on various hardware. Additionally, it offers secondary development capabilities, allowing you to train and fine-tune models on your own dataset, with seamless integration of the trained models.
 
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition/01.png"/>
+
 <b>The General Table Recognition Pipeline v2 includes mandatory modules such as table structure recognition, table classification, table cell localization, text detection, and text recognition, as well as optional modules like layout area detection, document image orientation classification, and text image correction.</b>
 <b>If you prioritize model accuracy, choose a model with higher accuracy; if you care more about inference speed, choose a model with faster inference speed; if you are concerned about model storage size, choose a model with a smaller storage size.</b>
+
+<details><summary> 👉Model List Details</summary>
+
 <p><b>Table Recognition Module Models:</b></p>
 <table>
 <tr>
@@ -41,49 +45,53 @@ The General Table Recognition Pipeline v2 is designed to solve table recognition
 </tr>
 </table>
 <b>Note: The above accuracy metrics are measured from the high-difficulty Chinese table recognition dataset internally built by PaddleX. The GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision type. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b>
+
 <p><b>Table Classification Module Models:</b></p>
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>Top1 Acc(%)</th>
-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>GPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>Model Storage Size (M)</th>
 </tr>
 <tr>
-<td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/CLIP_vit_base_patch16_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">Training Model</a></td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/CLIP_vit_base_patch16_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">Training Model</a></td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
 <td>6.6M</td>
 </tr>
 </table>
-<p><b>Note: The above accuracy metrics are measured on PaddleX's internally built table classification dataset. All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
+<p><b>Note: The above accuracy metrics are measured from the internal table classification dataset built by PaddleX. All models' GPU inference time is based on an NVIDIA Tesla T4 machine, with a precision type of FP32. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and a precision type of FP32.</b></p>
+
 <p><b>Table Cell Detection Module Models:</b></p>
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>mAP(%)</th>
-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>GPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>Model Storage Size (M)</th>
-<th>Description</th>
+<th>Introduction</th>
 </tr>
 <tr>
 <td>RT-DETR-L_wired_table_cell_det</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wired_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">Training Model</a></td>
-<td rowspan="2">--</td>
-<td rowspan="2">--</td>
-<td rowspan="2">--</td>
-`<td rowspan="2">124M</td>
-<td rowspan="2">RT-DETR is the first real-time end-to-end object detection model. The Baidu PaddlePaddle Vision Team used RT-DETR-L as the base model and pre-trained it on a self-built table cell detection dataset, achieving good performance in detecting both wired and wireless tables.</td>
+<td rowspan="2">82.7</td>
+<td rowspan="2">35.00 / 10.45</td>
+<td rowspan="2">495.51 / 495.51</td>
+<td rowspan="2">124M</td>
+<td rowspan="2">RT-DETR is the first real-time end-to-end object detection model. The Baidu PaddlePaddle Vision Team, based on RT-DETR-L as the base model, has completed pretraining on a self-built table cell detection dataset, achieving good performance for both wired and wireless table cell detection.
+</td>
 </tr>
 <tr>
 <td>RT-DETR-L_wireless_table_cell_det</td>
-<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wireless_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">Training Model</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wireless_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wireless_table_cell_det_pretrained.pdparams">Training Model</a></td>
 </tr>
 </table>
-<p><b>Note: The above accuracy metrics are measured on PaddleX's internally built table cell detection dataset. All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
+<p><b>Note: The above accuracy metrics are measured from the internal table cell detection dataset of PaddleX. All model GPU inference times are based on an NVIDIA Tesla T4 machine, with precision type FP32. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32. Considering that the table cell detection module needs to be integrated into the table recognition production line v2 for practical applications, the table cell detection results output from the table recognition production line v2 are used to calculate the mAP accuracy.</b></p>
+
 <p><b>Text Detection Module Models:</b></p>
 <table>
 <thead>
@@ -400,6 +408,8 @@ SVTRv2 is a server-side text recognition model developed by the OpenOCR team fro
 </table>
 <p><b>Note: The accuracy metrics above are evaluated on a self-built dataset covering multiple scenarios such as documents and certificates, including 1000 images. GPU inference time is based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
 
+</details>
+
 
 ## 2. Quick Start
 All model production lines provided by PaddleX can be quickly experienced. You can use the command line or Python locally to experience the effect of the general table recognition production line v2.
@@ -420,7 +430,7 @@ paddlex --pipeline table_recognition_v2 \
         --device gpu:0
 ```
 
-The relevant parameter descriptions can be referred to in the [2.2.2 Integration via Python Script](#222-python-script-integration) for parameter descriptions.
+The relevant parameter descriptions can be referred to in the [2.2 Integration via Python Script](#22-python-script-integration) for parameter descriptions.
 
 <details><summary>👉 <b>After running, the result is: (Click to expand)</b></summary>
 
@@ -559,10 +569,12 @@ The relevant parameter descriptions can be referred to in the [2.2.2 Integration
 
 The explanation of the running result parameters can refer to the result interpretation in [2.2.2 Python Script Integration](#222-python-script-integration).
 
-</details>
 
 The visualization results are saved under `save_path`, where the visualization result of table recognition is as follows:
-<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/03.png"/>
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/01.jpg">
+
+</details>
 
 ### 2.2 Python Script Integration
 * The above command line is for a quick experience to view the effect. Generally, in a project, integration through code is often required. You can complete the pipeline's fast inference with just a few lines of code. The inference code is as follows:
@@ -570,7 +582,7 @@ The visualization results are saved under `save_path`, where the visualization r
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="table_recognition_v2")
+pipeline = create_pipeline(pipeline="table_recognition_v2")
 
 output = pipeline.predict(
     input="table_recognition.jpg",
@@ -990,7 +1002,7 @@ If you have obtained the configuration file, you can customize the settings for
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="./my_path/table_recognition_v2.yaml")
+pipeline = create_pipeline(pipeline="./my_path/table_recognition_v2.yaml")
 
 output = pipeline.predict(
     input="table_recognition.jpg",

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md

@@ -39,6 +39,7 @@ comments: true
 </tr>
 </table>
 <b>注：以上精度指标测量自 PaddleX 内部自建高难度中文表格识别数据集。所有模型 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>
+
 <p><b>表格分类模块模型：</b></p>
 <table>
 <tr>
@@ -50,13 +51,15 @@ comments: true
 </tr>
 <tr>
 <td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/CLIP_vit_base_patch16_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
 <td>6.6M</td>
 </tr>
 </table>
-<p><b>注：以上精度指标测量自 PaddleX 内部自建表格分类数据集。所有模型 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></p>
+<p><b>注：以上精度指标测量自 PaddleX 内部自建评测数据集。所有模型 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></p>
+
+
 <p><b>表格单元格检测模块模型：</b></p>
 <table>
 <tr>
@@ -70,19 +73,21 @@ comments: true
 <tr>
 <td>RT-DETR-L_wired_table_cell_det</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wired_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td>
-<td rowspan="2">--</td>
-<td rowspan="2">--</td>
-<td rowspan="2">--</td>
+<td rowspan="2">82.7</td>
+<td rowspan="2">35.00 / 10.45</td>
+<td rowspan="2">495.51 / 495.51</td>
 <td rowspan="2">124M</td>
 <td rowspan="2">RT-DETR 是第一个实时的端到端目标检测模型。百度飞桨视觉团队基于 RT-DETR-L 作为基础模型，在自建表格单元格检测数据集上完成预训练，实现了对有线表格、无线表格均有较好性能的表格单元格检测。
 </td>
 </tr>
 <tr>
 <td>RT-DETR-L_wireless_table_cell_det</td>
-<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wireless_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-L_wireless_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wireless_table_cell_det_pretrained.pdparams">训练模型</a></td>
 </tr>
 </table>
-<p><b>注：以上精度指标测量自 PaddleX 内部自建表格单元格检测数据集。所有模型 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></p>
+<p><b>注：以上精度指标测量自 PaddleX 内部自建评测集。所有模型 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。考虑到实际应用时，表格单元格检测模块需要被集成在表格识别产线v2中使用，因此采用表格识别产线v2输出的表格单元格检测结果计算 mAP 精度。</b></p>
+
+
 <p><b>文本检测模块模型：</b></p>
 <table>
 <thead>
@@ -417,7 +422,7 @@ paddlex --pipeline table_recognition_v2 \
         --device gpu:0
 ```
 
-相关的参数说明可以参考[2.2.2 Python脚本方式集成](#222-python脚本方式集成)中的参数说明。
+相关的参数说明可以参考[2.2 Python脚本方式集成](#22-python脚本方式集成)中的参数说明。
 
 <details><summary>👉 <b>运行后，得到的结果为：（点击展开）</b></summary>
 
@@ -553,12 +558,12 @@ paddlex --pipeline table_recognition_v2 \
        [503, 121],
        [448, 121]], dtype=int16)], 'rec_texts': ['CRuncover', 'Dres', '连续工作3', '取出来放在网上', '没想', '江、整江等八大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': [0.9951260685920715, 0.9943379759788513, 0.9968608021736145, 0.9978817105293274, 0.9985721111297607, 0.9616036415100098, 0.9977153539657593, 0.987593948841095, 0.9906861186027527, 0.9959743618965149, 0.9970152378082275, 0.9977849721908569, 0.9984450936317444], 'rec_boxes': [array([234,   6, 316,  25], dtype=int16), array([38, 39, 73, 57], dtype=int16), array([122,  32, 201,  58], dtype=int16), array([227,  34, 346,  57], dtype=int16), array([351,  34, 391,  58], dtype=int16), array([417,  35, 534,  58], dtype=int16), array([34, 70, 78, 90], dtype=int16), array([287,  70, 328,  90], dtype=int16), array([454,  69, 496,  90], dtype=int16), array([ 17, 101,  95, 124], dtype=int16), array([144, 101, 178, 122], dtype=int16), array([278, 101, 338, 124], dtype=int16), array([448, 101, 503, 121], dtype=int16)]}}]}}
 ```
-运行结果参数说明可以参考[2.2.2 Python脚本方式集成](#222-python脚本方式集成)中的结果解释。
-
-</details>
+运行结果参数说明可以参考[2.2 Python脚本方式集成](#22-python脚本方式集成)中的结果解释。
 
 可视化结果保存在`save_path`下，其中表格识别的可视化结果如下：
-<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/03.png"/>
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/01.jpg">
+
+</details>
 
 
 ### 2.2 Python脚本方式集成
@@ -567,7 +572,7 @@ paddlex --pipeline table_recognition_v2 \
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="table_recognition_v2")
+pipeline = create_pipeline(pipeline="table_recognition_v2")
 
 output = pipeline.predict(
     input="table_recognition.jpg",
@@ -969,7 +974,7 @@ paddlex --get_pipeline_config table_recognition_v2 --save_path ./my_path
 ```python
 from paddlex import create_pipeline
 
-pipeline = create_pipeline(pipeline_name="./my_path/table_recognition_v2.yaml")
+pipeline = create_pipeline(pipeline="./my_path/table_recognition_v2.yaml")
 
 output = pipeline.predict(
     input="table_recognition.jpg",
@@ -992,7 +997,7 @@ for res in output:
 ## 3. 开发集成/部署
 如果产线可以达到您对产线推理速度和精度的要求，您可以直接进行开发集成/部署。
 
-若您需要将产线直接应用在您的Python项目中，可以参考 [2.2 Python脚本方式](#22-python脚本方式集成)中的示例代码。
+若您需要将产线直接应用在您的Python项目中，可以参考 [2.2 Python脚本方式集成](#22-python脚本方式集成)中的示例代码。
 
 此外，PaddleX 也提供了其他三种部署方式，详细说明如下：