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# General Table Recognition v2 Pipeline Tutorial

## 1. Introduction to General Table Recognition v2 Pipeline
Table recognition is a technology that automatically identifies and extracts table content and its structure from documents or images. It is widely used in data entry, information retrieval, and document analysis. By using computer vision and machine learning algorithms, table recognition can convert complex table information into an editable format, making it easier for users to further process and analyze data.

The General Table Recognition v2 Pipeline (PP-TableMagic) 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 custom development capabilities, allowing you to train and fine-tune models on your own dataset, with seamless integration of the trained models. <b> In addition, the General Table Recognition v2 Pipeline also supports the use of end-to-end table structure recognition models (e.g. SLANet, SLANet_plus, etc.), and supports independent configuration of table recognition for wired and wireless table, allowing developers to freely select and combine the best table recognition solutions.</b>

<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/01.png"/>

<b>The General Table Recognition v2 Pipeline 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 Structure Recognition Module Models:</b></p>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Accuracy (%)</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>Introduction</th>
</tr>
<tr>
<td>SLANeXt_wired</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANeXt_wired_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wired_pretrained.pdparams">Training 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">The SLANeXt series are the latest table structure recognition models developed by the PaddlePaddle Vision Team. Compared to SLANet and SLANet_plus, SLANeXt focuses on table structure recognition and has dedicated weights trained for wired and wireless tables, significantly improving the recognition ability for both types, especially for wired tables.</td>
</tr>
<tr>
<td>SLANeXt_wireless</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANeXt_wireless_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wireless_pretrained.pdparams">Training Model</a></td>
</tr>
</table>

<p><b>Table Classification Module Models:</b></p>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Top1 Acc(%)</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/paddle3.0.0/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>Table Cell Detection Module Models:</b></p>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(%)</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>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/paddle3.0.0/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">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 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 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/paddle3.0.0/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>Text Detection Module Models:</b></p>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Detection Hmean (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PP-OCRv5_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">Training Model</a></td>
<td>83.8</td>
<td>89.55 / 70.19</td>
<td>371.65 / 371.65</td>
<td>84.3</td>
<td>PP-OCRv5 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers</td>
</tr>
<tr>
<td>PP-OCRv5_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">Training Model</a></td>
<td>79.0</td>
<td>8.79 / 3.13</td>
<td>51.00 / 28.58</td>
<td>4.7</td>
<td>PP-OCRv5 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices</td>
</tr>
<tr>
<td>PP-OCRv4_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">Training Model</a></td>
<td>69.2</td>
<td>83.34 / 80.91</td>
<td>442.58 / 442.58</td>
<td>109</td>
<td>PP-OCRv4 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers</td>
</tr>
<tr>
<td>PP-OCRv4_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">Training Model</a></td>
<td>63.8</td>
<td>8.79 / 3.13</td>
<td>51.00 / 28.58</td>
<td>4.7</td>
<td>PP-OCRv4 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices</td>
</tr>
</tbody>
</table>

<p><b>Text Recognition Module Models:</b></p>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy (%)</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>Introduction</th>
</tr>
<tr>
<td>PP-OCRv5_server_rec</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_rec_pretrained.pdparams">Training Model</a></td>
<td>86.38</td>
<td>8.45/2.36</td>
<td>122.69/122.69</td>
<td>81 M</td>
<td rowspan="2">PP-OCRv5_rec is a next-generation text recognition model. This model is dedicated to efficiently and accurately supporting four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—with a single model. It supports complex text scenarios, including handwritten, vertical text, pinyin, and rare characters. While maintaining recognition accuracy, it also balances inference speed and model robustness, providing efficient and precise technical support for document understanding in various scenarios.</td>
</tr>
<tr>
<td>PP-OCRv5_mobile_rec</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams">Training Model</a></td>
<td>81.29</td>
<td>1.46/5.43</td>
<td>5.32/91.79</td>
<td>16 M</td>
</tr>
<tr>
<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_doc_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>81.53</td>
<td>6.65 / 2.38</td>
<td>32.92 / 32.92</td>
<td>74.7 M</td>
<td>PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the ability to recognize some traditional Chinese characters, Japanese, and special characters, and can support the recognition of more than 15,000 characters. In addition to improving the text recognition capability related to documents, it also enhances the general text recognition capability.</td>
</tr>
<tr>
<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Training Model</a></td>
<td>78.74</td>
<td>4.82 / 1.20</td>
<td>16.74 / 4.64</td>
<td>10.6 M</td>
<td>
The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.</td>
</tr>
<tr>
<td>PP-OCRv4_server_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Training Model</a></td>
<td>80.61 </td>
<td>6.58 / 2.43</td>
<td>33.17 / 33.17</td>
<td>71.2 M</td>
<td>The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.</td>
</tr>
<tr>
<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/en_PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>70.39</td>
<td>4.81 / 0.75</td>
<td>16.10 / 5.31</td>
<td>6.8 M</td>
<td>The ultra-lightweight English recognition model, trained based on the PP-OCRv4 recognition model, supports the recognition of English letters and numbers.</td>
</tr>
</table>

> ❗ The above list features the <b>4 core models</b> that the text recognition module primarily supports. In total, this module supports <b>18 models</b>. The complete list of models is as follows:

<details><summary> 👉Model List Details</summary>

* <b>PP-OCRv5 Multi-Scenario Model</b>

<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Chinese Recognition Avg Accuracy (%)</th>
<th>English Recognition Avg Accuracy (%)</th>
<th>Traditional Chinese Recognition Avg Accuracy (%)</th>
<th>Japanese Recognition Avg Accuracy (%)</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 Size (M)</th>
<th>Description</th>
</tr>
<tr>
<td>PP-OCRv5_server_rec</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_rec_pretrained.pdparams">Training Model</a></td>
<td>86.38</td>
<td>64.70</td>
<td>93.29</td>
<td>60.35</td>
<td>1.46/5.43</td>
<td>5.32/91.79</td>
<td>81 M</td>
<td rowspan="2">PP-OCRv5_rec is a next-generation text recognition model. This model efficiently and accurately supports four major languages with a single model: Simplified Chinese, Traditional Chinese, English, and Japanese. It recognizes complex text scenarios including handwritten, vertical text, pinyin, and rare characters. While maintaining recognition accuracy, it balances inference speed and model robustness, providing efficient and precise technical support for document understanding in various scenarios.</td>
</tr>
<tr>
<td>PP-OCRv5_mobile_rec</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams">Training Model</a></td>
<td>81.29</td>
<td>66.00</td>
<td>83.55</td>
<td>54.65</td>
<td>1.46/5.43</td>
<td>5.32/91.79</td>
<td>16 M</td>
</tr>
</table>

* <b>Chinese Recognition Model</b>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy(%)</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>Introduction</th>
</tr>
<tr>
<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_doc_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>81.53</td>
<td>6.65 / 2.38</td>
<td>32.92 / 32.92</td>
<td>74.7 M</td>
<td>PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the recognition capabilities for some traditional Chinese characters, Japanese, and special characters. The number of recognizable characters is over 15,000. In addition to the improvement in document-related text recognition, it also enhances the general text recognition capability.</td>
</tr>
<tr>
<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Training Model</a></td>
<td>78.74</td>
<td>4.82 / 1.20</td>
<td>16.74 / 4.64</td>
<td>10.6 M</td>
<td>The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.</td>
</tr>
<tr>
<td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Training Model</a></td>
<td>80.61 </td>
<td>6.58 / 2.43</td>
<td>33.17 / 33.17</td>
<td>71.2 M</td>
<td>The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.</td>
</tr>
<tr>
<td>PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>72.96</td>
<td>5.87 / 1.19</td>
<td>9.07 / 4.28</td>
<td>9.2 M</td>
<td>PP-OCRv3’s lightweight recognition model is designed for high inference efficiency and can be deployed on a variety of hardware devices, including edge devices.</td>
</tr>
</table>

<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy(%)</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>Introduction</th>
</tr>
<tr>
<td>ch_SVTRv2_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_SVTRv2_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">Training Model</a></td>
<td>68.81</td>
<td>8.08 / 2.74</td>
<td>50.17 / 42.50</td>
<td>73.9 M</td>
<td rowspan="1">
SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan University's Visual and Learning Laboratory (FVL). It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the A list is 6% higher than that of PP-OCRv4.
</td>
</tr>
</table>

<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy(%)</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>Introduction</th>
</tr>
<tr>
<td>ch_RepSVTR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_RepSVTR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">Training Model</a></td>
<td>ch_RepSVTR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_RepSVTR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">Training Model</a></td>
<td>65.07</td>
<td>5.93 / 1.62</td>
<td>20.73 / 7.32</td>
<td>22.1 M</td>
<td rowspan="1">    The RepSVTR text recognition model is a mobile text recognition model based on SVTRv2. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the B list is 2.5% higher than that of PP-OCRv4, with the same inference speed.</td>
</tr>
</table>

* <b>English Recognition Model</b>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy(%)</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>Introduction</th>
</tr>
<tr>
<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/en_PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td> 70.39</td>
<td>4.81 / 0.75</td>
<td>16.10 / 5.31</td>
<td>6.8 M</td>
<td>The ultra-lightweight English recognition model trained based on the PP-OCRv4 recognition model supports the recognition of English and numbers.</td>
</tr>
<tr>
<td>en_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/en_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>70.69</td>
<td>5.44 / 0.75</td>
<td>8.65 / 5.57</td>
<td>7.8 M </td>
<td>The ultra-lightweight English recognition model trained based on the PP-OCRv3 recognition model supports the recognition of English and numbers.</td>
</tr>
</table>

* <b>Multilingual Recognition Model</b>
<table>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Recognition Avg Accuracy(%)</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>Introduction</th>
</tr>
<tr>
<td>korean_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/korean_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>60.21</td>
<td>5.40 / 0.97</td>
<td>9.11 / 4.05</td>
<td>8.6 M</td>
<td>The ultra-lightweight Korean recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Korean and numbers. </td>
</tr>
<tr>
<td>japan_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/japan_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>45.69</td>
<td>5.70 / 1.02</td>
<td>8.48 / 4.07</td>
<td>8.8 M </td>
<td>The ultra-lightweight Japanese recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Japanese and numbers.</td>
</tr>
<tr>
<td>chinese_cht_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/chinese_cht_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>82.06</td>
<td>5.90 / 1.28</td>
<td>9.28 / 4.34</td>
<td>9.7 M </td>
<td>The ultra-lightweight Traditional Chinese recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Traditional Chinese and numbers.</td>
</tr>
<tr>
<td>te_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/te_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>95.88</td>
<td>5.42 / 0.82</td>
<td>8.10 / 6.91</td>
<td>7.8 M </td>
<td>The ultra-lightweight Telugu recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Telugu and numbers.</td>
</tr>
<tr>
<td>ka_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ka_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>96.96</td>
<td>5.25 / 0.79</td>
<td>9.09 / 3.86</td>
<td>8.0 M </td>
<td>The ultra-lightweight Kannada recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Kannada and numbers.</td>
</tr>
<tr>
<td>ta_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ta_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>76.83</td>
<td>5.23 / 0.75</td>
<td>10.13 / 4.30</td>
<td>8.0 M </td>
<td>The ultra-lightweight Tamil recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Tamil and numbers.</td>
</tr>
<tr>
<td>latin_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/latin_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>76.93</td>
<td>5.20 / 0.79</td>
<td>8.83 / 7.15</td>
<td>7.8 M</td>
<td>The ultra-lightweight Latin recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Latin script and numbers.</td>
</tr>
<tr>
<td>arabic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/arabic_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>73.55</td>
<td>5.35 / 0.79</td>
<td>8.80 / 4.56</td>
<td>7.8 M</td>
<td>The ultra-lightweight Arabic script recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Arabic script and numbers.</td>
</tr>
<tr>
<td>cyrillic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/cyrillic_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>94.28</td>
<td>5.23 / 0.76</td>
<td>8.89 / 3.88</td>
<td>7.9 M  </td>
<td>
The ultra-lightweight cyrillic alphabet recognition model trained based on the PP-OCRv3 recognition model supports the recognition of cyrillic letters and numbers.</td>
</tr>
<tr>
<td>devanagari_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/devanagari_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
<td>96.44</td>
<td>5.22 / 0.79</td>
<td>8.56 / 4.06</td>
<td>7.9 M  </td>
<td>The ultra-lightweight Devanagari script recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Devanagari script and numbers.</td>
</tr>
</table>
</details>
<p><b>Layout Region Detection Module Models (Optional):</b></p>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(0.5)（%）</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
<td>90.4</td>
<td>34.6244 / 10.3945</td>
<td>510.57 / -</td>
<td>123.76 M</td>
<td>A high-precision layout region localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on RT-DETR-L.</td>
</tr>
<tr>
<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
<td>75.2</td>
<td>13.3259 / 4.8685</td>
<td>44.0680 / 44.0680</td>
<td>22.578</td>
<td>A layout region localization model with balanced accuracy and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on PicoDet-L.</td>
</tr>
<tr>
<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
<td>70.9</td>
<td>8.3008 / 2.3794</td>
<td>10.0623 / 9.9296</td>
<td>4.834</td>
<td>A high-efficiency layout region localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on PicoDet-S.</td>
</tr>
</tbody>
</table>

> ❗ The above list includes the <b>3 core models</b> that are the focus of the layout detection module. The module supports a total of <b>11 full models</b>, including multiple predefined models with different categories. The complete list of models is as follows:
<details><summary> 👉Model List Details</summary>
* <b>Table Layout Detection Models</b>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(0.5) (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Training Model</a></td>
<td>97.5</td>
<td>8.02 / 3.09</td>
<td>23.70 / 20.41</td>
<td>7.4 M</td>
<td>A high-efficiency layout area localization model trained on a self-built dataset based on PicoDet-1x, capable of locating table areas.</td>
</tr>
</tbody></table>

* <b>3-Class Layout Detection Model, Including Tables, Images, and Stamps</b>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(0.5) (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Training Model</a></td>
<td>88.2</td>
<td>8.99 / 2.22</td>
<td>16.11 / 8.73</td>
<td>4.8</td>
<td>A high-efficiency layout area localization model trained on a self-built dataset based on PicoDet-S lightweight model, suitable for Chinese and English papers, magazines, and research reports.</td>
</tr>
<tr>
<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Training Model</a></td>
<td>89.0</td>
<td>13.05 / 4.50</td>
<td>41.30 / 41.30</td>
<td>22.6</td>
<td>A balanced efficiency and accuracy layout area localization model trained on a self-built dataset based on PicoDet-L, suitable for Chinese and English papers, magazines, and research reports.</td>
</tr>
<tr>
<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Training Model</a></td>
<td>95.8</td>
<td>114.93 / 27.71</td>
<td>947.56 / 947.56</td>
<td>470.1</td>
<td>A high-precision layout area localization model trained on a self-built dataset based on RT-DETR-H, suitable for Chinese and English papers, magazines, and research reports.</td>
</tr>
</tbody></table>

* <b>5-Class English Document Area Detection Model, Including Text, Titles, Tables, Images, and Lists</b>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(0.5) (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Training Model</a></td>
<td>97.8</td>
<td>9.03 / 3.10</td>
<td>25.82 / 20.70</td>
<td>7.4</td>
<td>A high-efficiency English document layout area localization model trained on the PubLayNet dataset based on PicoDet-1x.</td>
</tr>
</tbody></table>

<b>17-class layout detection model, covering 17 common categories, including: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal</b>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>mAP(0.5) (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Training Model</a></td>
<td>87.4</td>
<td>9.11 / 2.12</td>
<td>15.42 / 9.12</td>
<td>4.8</td>
<td>A high-efficiency layout region detection model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on the lightweight PicoDet-S model</td>
</tr>
<tr>
<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
<td>89.0</td>
<td>13.50 / 4.69</td>
<td>43.32 / 43.32</td>
<td>22.6</td>
<td>A balanced efficiency and accuracy layout region detection model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on PicoDet-L</td>
</tr>
<tr>
<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
<td>98.3</td>
<td>115.29 / 104.09</td>
<td>995.27 / 995.27</td>
<td>470.2</td>
<td>A high-precision layout region detection model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on RT-DETR-H</td>
</tr>
</tbody>
</table>
</details>
<p><b>Text Image Correction Module Model (Optional):</b></p>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>MS-SSIM (%)</th>
<th>Model Storage Size (M)</th>
<th>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>UVDoc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UVDoc_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UVDoc_pretrained.pdparams">Training Model</a></td>
<td>54.40</td>
<td>30.3 M</td>
<td>A high-precision text image rectification model</td>
</tr>
</tbody>
</table>

<p><b>Document Image Orientation Classification Module Model (Optional):</b></p>
<table>
<thead>
<tr>
<th>Model</th><th>Model Download Link</th>
<th>Top-1 Acc (%)</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>Introduction</th>
</tr>
</thead>
<tbody>
<tr>
<td>PP-LCNet_x1_0_doc_ori</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">Training Model</a></td>
<td>99.06</td>
<td>2.31 / 0.43</td>
<td>3.37 / 1.27</td>
<td>7</td>
<td>A document image classification model based on PP-LCNet_x1_0, with four categories: 0 degrees, 90 degrees, 180 degrees, and 270 degrees</td>
</tr>
</tbody>
</table>

<strong>Test Environment Description:</strong>

  <ul>
      <li><b>Performance Test Environment</b>
          <ul>
            <li><strong>Test Dataset：</strong>
                        <ul>
                         <li>Document Image Orientation Classification Module: A self-built dataset using PaddleX, covering multiple scenarios such as ID cards and documents, containing 1000 images.</li>
                         <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. </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>
                         <li>Table Classification Model: A self-built evaluation dataset by PaddleX.</li>
                         <li>Text Detection Model: A self-built Chinese dataset by PaddleOCR, covering multiple scenarios such as street views, web images, documents, and handwriting, with 500 images for detection.</li>
                         <li>Chinese Recognition Model: A self-built Chinese dataset by PaddleOCR, covering multiple scenarios such as street views, web images, documents, and handwriting, with 11,000 images for text recognition.</li>
                        <li>ch_SVTRv2_rec: Evaluation set A for "OCR End-to-End Recognition Task" in the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a></li>
                          <li>ch_RepSVTR_rec: Evaluation set B for "OCR End-to-End Recognition Task" in the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a>.</li>
                         <li>English Recognition Model: A self-built English dataset by PaddleX.</li>
                         <li>Multilingual Recognition Model: A self-built multilingual dataset by PaddleX.</li>
                        </ul>
                </li>
              <li><strong>Hardware Configuration：</strong>
                  <ul>
                      <li>GPU: NVIDIA Tesla T4</li>
                      <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
                      <li>Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2</li>
                  </ul>
              </li>
          </ul>
      </li>
      <li><b>Inference Mode Description</b></li>
  </ul>

<table border="1">
    <thead>
        <tr>
            <th>Mode</th>
            <th>GPU Configuration </th>
            <th>CPU Configuration </th>
            <th>Acceleration Technology Combination</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Normal Mode</td>
            <td>FP32 Precision / No TRT Acceleration</td>
            <td>FP32 Precision / 8 Threads</td>
            <td>PaddleInference</td>
        </tr>
        <tr>
            <td>High-Performance Mode</td>
            <td>Optimal combination of pre-selected precision types and acceleration strategies</td>
            <td>FP32 Precision / 8 Threads</td>
            <td>Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.)</td>
        </tr>
    </tbody>
</table>

</details>


## 2. Quick Start
All model pipelines 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 v2 Pipeline.

### 2.1 Online Experience
Online experience is not supported at the moment.

### 2.2 Local Experience
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). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.

### 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)  and replace `--input` with the local path for prediction.

```bash
paddlex --pipeline table_recognition_v2 \
        --use_doc_orientation_classify=False \
        --use_doc_unwarping=False \
        --input table_recognition_v2.jpg \
        --save_path ./output \
        --device gpu:0
```

<details><summary>👉 <b>After running, the result obtained is: (Click to expand)</b></summary>

```
{'res': {'input_path': 'table_recognition_v2.jpg', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True, 'use_ocr_model': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 8, 'label': 'table', 'score': 0.86655592918396, 'coordinate': [0.0125130415, 0.41920784, 1281.3737, 585.3884]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'rec_boxes': array([[   9, ...,   59],
       ...,
       [1046, ...,  573]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([ 0.13052222, ..., 73.08310249]), array([104.43082511, ...,  73.27777413]), array([319.39041221, ...,  73.30439308]), array([424.2436837 , ...,  73.44736794]), array([580.75836265, ...,  73.24003914]), array([723.04370201, ...,  73.22717598]), array([984.67315757, ...,  73.20420387]), array([1.25130415e-02, ..., 5.85419208e+02]), array([984.37072837, ..., 137.02281502]), array([984.26586998, ..., 201.22290352]), array([984.24017417, ..., 585.30775765]), array([1039.90606773, ...,  265.44664314]), array([1039.69549644, ...,  329.30540779]), array([1039.66546714, ...,  393.57319954]), array([1039.5122689 , ...,  457.74644783]), array([1039.55535972, ...,  521.73030403]), array([1039.58612144, ...,  585.09468392])], 'pred_html': '<html><body><table><tbody><tr><td>部门</td><td></td><td>报销人</td><td></td><td>报销事由</td><td></td><td colspan="2">批准人：</td></tr><tr><td colspan="6" rowspan="8"></td><td colspan="2">单据 张</td></tr><tr><td colspan="2">合计金额 元</td></tr><tr><td rowspan="6">其 中</td><td>车费票</td></tr><tr><td>火车费票</td></tr><tr><td>飞机票</td></tr><tr><td>旅住宿费</td></tr><tr><td>其他</td></tr><tr><td>补贴</td></tr></tbody></table></body></html>', 'table_ocr_pred': {'rec_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_boxes': array([[   9, ...,   59],
       ...,
       [1046, ...,  573]], dtype=int16)}}]}}
```

The explanation of the running result parameters can refer to the result interpretation in [2.2.2 Python Script Integration](#222-python-script-integration). Supports specifying multiple devices simultaneously for parallel inference. For details, please refer to the documentation on pipeline parallel inference.


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_v2/02.jpg">

</details>

### 2.2 Integration via Python Script
* The above command line is for a quick experience to view the results. 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:

```python
from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="table_recognition_v2")

output = pipeline.predict(
    input="table_recognition_v2.jpg",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
)

for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_xlsx("./output/")
    res.save_to_html("./output/")
    res.save_to_json("./output/")
```

In the above Python script, the following steps are executed:

(1) The `create_pipeline()` function is used to instantiate a General Table Recognition v2 Pipeline object. The specific parameter descriptions are as follows:

<table>
<thead>
<tr>
<th>Parameter</th>
<th>Parameter Description</th>
<th>Parameter Type</th>
<th>Default Value</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>pipeline</code></td>
<td>The name of the pipeline or the path to the pipeline configuration file. If it is a pipeline name, it must be a pipeline supported by PaddleX.</td>
<td><code>str</code></td>
<td><code>None</code></td>
</tr>
<tr>
<td><code>config</code></td>
<td>The specific configuration information of the pipeline (if set simultaneously with <code>pipeline</code>, it has higher priority than <code>pipeline</code>, and the pipeline 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>device</code></td>
<td>The inference device for the pipeline. It supports specifying specific GPU card numbers, such as "gpu:0", specific card numbers for other hardware, such as "npu:0", and CPU like "cpu". Supports specifying multiple devices simultaneously for parallel inference. For details, please refer to <a href="../../instructions/parallel_inference.en.md#specifying-multiple-inference-devices">Pipeline Parallel Inference</a>.</td>
<td><code>str</code></td>
<td><code>gpu:0</code></td>
</tr>
<tr>
<td><code>use_hpip</code></td>
<td>Whether to enable the high-performance inference plugin. If set to <code>None</code>, the setting from the configuration file or <code>config</code> will be used.</td>
<td><code>bool</code></td>
<td>None</td>
<td><code>None</code></td>
</tr>
<tr>
<td><code>hpi_config</code></td>
<td>High-performance inference configuration</td>
<td><code>dict</code> | <code>None</code></td>
<td>None</td>
<td><code>None</code></td>
</tr>
</tbody>
</table>

(2) Call the `predict()` method of the General Table Recognition v2 Pipeline object for inference prediction. This method will return a `generator`. The parameters of the `predict()` method and their descriptions are as follows:


<table>
<thead>
<tr>
<th>Parameter</th>
<th>Description</th>
<th>Type</th>
<th>Options</th>
<th>Default Value</th>
</tr>
</thead>
<tr>
<td><code>input</code></td>
<td>Data to be predicted, supports multiple input types, required.</td>
<td><code>Python Var|str|list</code></td>
<td>
<ul>
<li><b>Python Var</b>: Image data represented by <code>numpy.ndarray</code>.</li>
<li><b>str</b>: Local path of image or PDF files, e.g., <code>/root/data/img.jpg</code>; <b>URL link</b>, such as the network URL of an image or PDF file: <a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg">Example</a>; <b>Local directory</b>, the directory should contain images to be predicted, e.g., <code>/root/data/</code> (currently, prediction for PDF files in directories is not supported; PDF files must specify the exact file path).</li>
<li><b>List</b>: List elements must be of the above types, such as <code>[numpy.ndarray, numpy.ndarray]</code>, <code>[“/root/data/img1.jpg”, “/root/data/img2.jpg”]</code>, <code>[“/root/data1”, “/root/data2”]</code>.</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<tr>
<td><code>use_doc_orientation_classify</code></td>
<td>Whether to use the document orientation classification module.</td>
<td><code>bool|None</code></td>
<td>
<ul>
<li><b>bool</b>: <code>True</code> or <code>False</code>.</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized by the production line will be used, initialized as <code>True</code>.</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<tr>
<td><code>use_doc_unwarping</code></td>
<td>Whether to use the document unwarping module.</td>
<td><code>bool|None</code></td>
<td>
<ul>
<li><b>bool</b>: <code>True</code> or <code>False</code>.</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized by the production line will be used, initialized as <code>True</code>.</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<tr>
<td><code>use_layout_detection</code></td>
<td>Whether to use the layout detection module.</td>
<td><code>bool|None</code></td>
<td>
<ul>
<li><b>bool</b>: <code>True</code> or <code>False</code>.</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized by the production line will be used, initialized as <code>True</code>.</li>
</ul>
</td>
<td><code>None</code></td>
</tr>

<td><code>text_det_limit_side_len</code></td>
<td>Image side length limit for text detection</td>
<td><code>int|None</code></td>
<td>
<ul>
<li><b>int</b>: Any integer greater than <code>0</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>960</code>;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>text_det_limit_type</code></td>
<td>Type of image side length limit for text detection</td>
<td><code>str|None</code></td>
<td>
<ul>
<li><b>str</b>: Supports <code>min</code> and <code>max</code>. <code>min</code> ensures the shortest side of the image is not less than <code>det_limit_side_len</code>, while <code>max</code> ensures the longest side is not greater than <code>limit_side_len</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>max</code>;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>text_det_thresh</code></td>
<td>Detection pixel threshold; in the output probability map, pixels with scores greater than this threshold will be considered as text pixels</td>
<td><code>float|None</code></td>
<td>
<ul>
<li><b>float</b>: Any floating-point number greater than <code>0</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>0.3</code>;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>text_det_box_thresh</code></td>
<td>Detection box threshold; the average score of all pixels within the detection box must be greater than this threshold for the result to be considered a text region</td>
<td><code>float|None</code></td>
<td>
<ul>
<li><b>float</b>: Any floating-point number greater than <code>0</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>0.6</code>;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>text_det_unclip_ratio</code></td>
<td>Text detection expansion ratio; this value determines the extent of expansion of the text region, with larger values resulting in greater expansion</td>
<td><code>float|None</code></td>
<td>
<ul>
<li><b>float</b>: Any floating-point number greater than <code>0</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>2.0</code>;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>text_rec_score_thresh</code></td>
<td>Text recognition threshold; text results with scores greater than this threshold will be retained</td>
<td><code>float|None</code></td>
<td>
<ul>
<li><b>float</b>: Any floating-point number greater than <code>0</code>;</li>
<li><b>None</b>: If set to <code>None</code>, the default value initialized in production will be used, initialized as <code>0.0</code>, meaning no threshold is set;</li>
</ul>
</td>
<td><code>None</code></td>
</tr>
<td><code>use_ocr_results_with_table_cells</code></td>
<td>Whether to enable cell OCR mode, when not enabled, the global OCR results are used to populate the HTML table, when enabled, based on the cell prediction results, the global OCR results are sliced and re-recognized based on the cell, to avoid the situation that the text is connected across the cell or missing. This mode will slightly increase the inference time consuming, and the effect may be different in different scenarios, please choose according to the actual situation.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>True</code></td>
</tr>
<td><code>use_e2e_wired_table_rec_model</code></td>
<td>Whether to enable the wired table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>False</code></td>
</tr>
<td><code>use_e2e_wireless_table_rec_model</code></td>
<td>Whether to enable the wireless table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>False</code></td>
</tr>
<td><code>use_table_orientation_classify</code></td>
<td>Whether to enable the table orientation classification mode, when it is not enabled, the table will be recognized directly based on the incoming image, when it is enabled, it will detect whether the table in the image is rotated or not, and be able to correct the orientation of the table by 90/180/270 degrees before recognizing the table. This mode will slightly increase the inference time, please choose according to the actual situation.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>True</code></td>
</tr>
<td><code>use_wired_table_cells_trans_to_html</code></td>
<td>Whether to enable the wired table cell direct to HTML mode, when it is not enabled, the HTML structure of the table is normally predicted by the table structure recognition module, when it is enabled, the HTML structure is constructed directly based on the positional arrangement and geometric relationship of the cells predicted by the wired table cell detection model. The effect of this model is different in different scenarios, please choose according to the actual situation. Generally speaking, it is recommended to enable this mode in scenarios where the wired table cell detection is very accurate, in order to obtain more accurate end-to-end table recognition results.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>False</code></td>
</tr>
<td><code>use_wireless_table_cells_trans_to_html</code></td>
<td>Whether to enable the wireless table cell direct to HTML mode, when it is not enabled, the HTML structure of the table is normally predicted by the table structure recognition module, when it is enabled, the HTML structure is constructed directly based on the positional arrangement and geometrical relationship of the cells predicted by the wireless table cell detection model. The effect of this model is different in different scenarios, please choose according to the actual situation. Generally speaking, it is recommended to enable this mode in scenarios where the wireless table cell detection is very accurate, in order to obtain more accurate end-to-end table recognition results.</td>
<td><code>bool</code></td>
<td>
<ul>
<li><b>bool</b>：<code>True</code> or <code>False</code>
<td><code>False</code></td>
</tr>
</table>

<b>If you need to use the end-to-end table structure recognition model, just replace the corresponding table structure recognition model with the end-to-end table structure recognition model in the pipeline config file, and then load the modified config file and modify the corresponding `predict()` method parameter</b>. For example, if you need to use SLANet_plus to do end-to-end table recognition for wireless tables, just replace `model_name` with SLANet_plus in `WirelessTableStructureRecognition` in the config file (as shown below) and specify `use_e2e_ wireless_table_rec_model=True` in the prediction, the rest of the parts do not need to be modified, at this time the wireless table cells detection model will not take effect, but directly use SLANet_plus for end-to-end table recognition.

```yaml
SubModules:
  WiredTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANeXt_wired
    model_dir: null

  WirelessTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANet_plus  # Replace with the end-to-end table structure recognition model
    model_dir: null

  WiredTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wired_table_cell_det
    model_dir: null

  WirelessTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wireless_table_cell_det
    model_dir: null
```

(3) Process the prediction results, where each sample's prediction result is represented as a corresponding Result object, and supports operations such as printing, saving as an image, saving as an `xlsx` file, saving as an `HTML` file, and saving as a `json` file:

<table>
<thead>
<tr>
<th>Method</th>
<th>Method Description</th>
<th>Parameter</th>
<th>Parameter Type</th>
<th>Parameter Description</th>
<th>Default Value</th>
</tr>
</thead>
<tr>
<td rowspan="3"><code>print()</code></td>
<td rowspan="3">Print the result to the terminal</td>
<td><code>format_json</code></td>
<td><code>bool</code></td>
<td>Whether to format the output content using <code>JSON</code> indentation</td>
<td><code>True</code></td>
</tr>
<tr>
<td><code>indent</code></td>
<td><code>int</code></td>
<td>Specify the indentation level to beautify the <code>JSON</code> data, making it more readable. Only effective when <code>format_json</code> is <code>True</code></td>
<td>4</td>
</tr>
<tr>
<td><code>ensure_ascii</code></td>
<td><code>bool</code></td>
<td>Control whether non-<code>ASCII</code> characters are escaped to <code>Unicode</code>. If set to <code>True</code>, all non-<code>ASCII</code> characters will be escaped; <code>False</code> retains the original characters. Only effective when <code>format_json</code> is <code>True</code></td>
<td><code>False</code></td>
</tr>
<tr>
<td rowspan="3"><code>save_to_json()</code></td>
<td rowspan="3">Save the result as a JSON file</td>
<td><code>save_path</code></td>
<td><code>str</code></td>
<td>The file path for saving. If it is a directory, the saved file will have the same name as the input file type</td>
<td>None</td>
</tr>
<tr>
<td><code>indent</code></td>
<td><code>int</code></td>
<td>Specify the indentation level to beautify the <code>JSON</code> data, making it more readable. Only effective when <code>format_json</code> is <code>True</code></td>
<td>4</td>
</tr>
<tr>
<td><code>ensure_ascii</code></td>
<td><code>bool</code></td>
<td>Control whether non-<code>ASCII</code> characters are escaped to <code>Unicode</code>. If set to <code>True</code>, all non-<code>ASCII</code> characters will be escaped; <code>False</code> retains the original characters. Only effective when <code>format_json</code> is <code>True</code></td>
<td><code>False</code></td>
</tr>
<tr>
<td><code>save_to_img()</code></td>
<td>Save the result as an image file</td>
<td><code>save_path</code></td>
<td><code>str</code></td>
<td>The file path for saving, supporting both directory and file paths</td>
<td>None</td>
</tr>
<tr>
<td><code>save_to_xlsx()</code></td>
<td>Save the result as an xlsx file</td>
<td><code>save_path</code></td>
<td><code>str</code></td>
<td>The file path for saving, supporting both directory and file paths</td>
<td>None</td>
</tr>
<tr>
<td><code>save_to_html()</code></td>
<td>Save the result as an HTML file</td>
<td><code>save_path</code></td>
<td><code>str</code></td>
<td>The file path for saving, supporting both directory and file paths</td>
<td>None</td>
</tr>
</table>

- Calling the `print()` method will print the results to the terminal, and the content printed to the terminal is explained as follows:

    - `input_path`: `(str)` The input path of the image to be predicted.

    - `page_index`: `(Union[int, None])` If the input is a PDF file, it indicates which page of the PDF is currently being processed; otherwise, it is `None`.

    - `model_settings`: `(Dict[str, bool])` Configuration parameters for the pipeline models.

        - `use_doc_preprocessor`: `(bool)` Controls whether to enable the document preprocessing sub-line.
        - `use_layout_detection`: `(bool)` Controls whether to enable the layout detection sub-line.
        - `use_ocr_model`: `(bool)` Controls whether to enable the OCR sub-line.
    - `layout_det_res`: `(Dict[str, Union[List[numpy.ndarray], List[float]]])` Output results of the layout detection sub-module. Only exists when `use_layout_detection=True`.
        - `input_path`: `(Union[str, None])` The image path accepted by the layout detection module. When the input is a `numpy.ndarray`, it is saved as `None`.
        - `page_index`: `(Union[int, None])` If the input is a PDF file, it indicates which page of the PDF is currently being processed; otherwise, it is `None`.
        - `boxes`: `(List[Dict])` A list of detected layout seal region boxes, with each element in the list containing the following fields:
            - `cls_id`: `(int)` The class ID of the detected box.
            - `score`: `(float)` The confidence score of the detected box.
            - `coordinate`: `(List[float])` The coordinates of the four vertices of the detected box, in the order of x1, y1, x2, y2, representing the x-coordinate of the top-left corner, the y-coordinate of the top-left corner, the x-coordinate of the bottom-right corner, and the y-coordinate of the bottom-right corner.
    - `doc_preprocessor_res`: `(Dict[str, Union[str, Dict[str, bool], int]])` Output results of the document preprocessing sub-line. Only exists when `use_doc_preprocessor=True`.
        - `input_path`: `(Union[str, None])` The image path accepted by the preprocessing sub-line. When the input is a `numpy.ndarray`, it is saved as `None`.
        - `model_settings`: `(Dict)` Model configuration parameters for the preprocessing sub-line.
            - `use_doc_orientation_classify`: `(bool)` Controls whether to enable document orientation classification.
            - `use_doc_unwarping`: `(bool)` Controls whether to enable document unwarping.
        - `angle`: `(int)` The predicted result of document orientation classification. When enabled, the values are [0,1,2,3], corresponding to [0°,90°,180°,270°]; when disabled, it is -1.

    - `dt_polys`: `(List[numpy.ndarray])` A list of polygon boxes for text detection. Each detection box is represented by a numpy array of 4 vertex coordinates, with the array shape being (4, 2) and data type being int16.

    - `dt_scores`: `(List[float])` A list of confidence scores for text detection boxes.

    - `text_det_params`: `(Dict[str, Dict[str, int, float]])` Configuration parameters for the text detection module.
        - `limit_side_len`: `(int)` The side length limit value for image preprocessing.
        - `limit_type`: `(str)` The processing method for side length limits.
        - `thresh`: `(float)` The confidence threshold for text pixel classification.
        - `box_thresh`: `(float)` The confidence threshold for text detection boxes.
        - `unclip_ratio`: `(float)` The expansion ratio for text detection boxes.
        - `text_type`: `(str)` The type of text detection, currently fixed as "general".

    - `text_rec_score_thresh`: `(float)` The filtering threshold for text recognition results.

    - `rec_texts`: `(List[str])` A list of text recognition results, containing only texts with confidence scores above `text_rec_score_thresh`.

    - `rec_scores`: `(List[float])` A list of confidence scores for text recognition, filtered by `text_rec_score_thresh`.

    - `rec_polys`: `(List[numpy.ndarray])` A list of text detection boxes filtered by confidence score, in the same format as `dt_polys`.

    - `rec_boxes`: `(numpy.ndarray)` An array of rectangular bounding boxes for detection boxes, with shape (n, 4) and dtype int16. Each row represents the [x_min, y_min, x_max, y_max] coordinates of a rectangular box, where (x_min, y_min) is the top-left corner and (x_max, y_max) is the bottom-right corner.

- Calling the `save_to_json()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.json`; if specified as a file, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, the `numpy.array` types will be converted to lists.

- Calling the `save_to_img()` method will save the visualization results to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}_ocr_res_img.{your_img_extension}`; if specified as a file, it will be saved directly to that file. (The pipeline usually contains many result images, it is not recommended to specify a specific file path directly, otherwise multiple images will be overwritten, leaving only the last image)

- Calling the `save_to_html()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.html`; if specified as a file, it will be saved directly to that file. In the General Table Recognition v2 Pipeline, the HTML form of the table in the image will be written to the specified HTML file.

- Calling the `save_to_xlsx()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.xlsx`; if specified as a file, it will be saved directly to that file. In the General Table Recognition v2 Pipeline, the Excel form of the table in the image will be written to the specified XLSX file.

* Additionally, it is also possible to obtain the visualization image with results and prediction results through attributes, as follows:

<table>
<thead>
<tr>
<th>Attribute</th>
<th>Description</th>
</tr>
</thead>
<tr>
<td rowspan="1"><code>json</code></td>
<td rowspan="1">Get the prediction result in <code>json</code> format.</td>
</tr>
<tr>
<td rowspan="2"><code>img</code></td>
<td rowspan="2">Get the visualization image in <code>dict</code> format.</td>
</tr>
</table>

- The prediction result obtained through the `json` attribute is of dict type, and the content is consistent with the content saved by calling the `save_to_json()` method.
- The prediction result returned by the `img` attribute is a dictionary. The keys are `table_res_img`, `ocr_res_img`, `layout_res_img`, and `preprocessed_img`, corresponding to four `Image.Image` objects in order: the visualization image of table recognition results, the visualization image of OCR results, the visualization image of layout detection results, and the visualization image of image preprocessing. If a sub-module is not used, the corresponding result image will not be included in the dictionary.

In addition, you can obtain the General Table Recognition v2 Pipeline configuration file and load the configuration file for prediction. You can execute the following command to save the result in `my_path`:

```
paddlex --get_pipeline_config table_recognition_v2 --save_path ./my_path
```

If you have obtained the configuration file, you can customize the settings for the General Table Recognition v2 Pipeline. Simply modify the `pipeline` parameter value in the `create_pipeline` method to the path of the pipeline configuration file. The example is as follows:

```python
from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="./my_path/table_recognition_v2.yaml")

output = pipeline.predict(
    input="table_recognition_v2.jpg",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
)

for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_xlsx("./output/")
    res.save_to_html("./output/")
    res.save_to_json("./output/")

```

<b>Note:</b> The parameters in the configuration file are the initialization parameters for the pipeline. If you want to change the initialization parameters of the General Table Recognition v2 Pipeline, you can directly modify the parameters in the configuration file and load the configuration file for prediction. Additionally, CLI prediction also supports passing in the configuration file by specifying the path with `--pipeline`.

## 3. Development Integration / Deployment
If the pipeline meets your requirements for inference speed and accuracy, you can proceed directly with development integration / deployment.

If you need to apply the pipeline directly in your Python project, you can refer to the example code in [2.2 Python Script Integration](#22-python脚本方式集成).

In addition, PaddleX also provides three other deployment methods, which are detailed as follows:

🚀 <b>High-Performance Inference</b>: In actual production environments, many applications have strict performance requirements for deployment strategies, especially in terms of response speed, to ensure efficient system operation and smooth user experience. To this end, PaddleX provides a high-performance inference plugin, which aims to deeply optimize the performance of model inference and pre/post-processing to significantly speed up the end-to-end process. For detailed information on high-performance inference, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).

☁️ <b>Serving Deployment</b>: Serving Deployment is a common form of deployment in actual production environments. By encapsulating the inference functionality as a service, clients can access these services through network requests to obtain inference results. PaddleX supports various serving deployment solutions for pipelines. For detailed information on serving deployment, please refer to the [PaddleX Serving Deployment Guide](../../../pipeline_deploy/serving.en.md).

Below are the API references for basic serving deployment and multi-language service invocation examples:

<details><summary>API Reference</summary>
<p>For the main operations provided by the service:</p>
<ul>
<li>The HTTP request method is POST.</li>
<li>Both the request body and response body are JSON data (JSON objects).</li>
<li>When the request is processed successfully, the response status code is <code>200</code>, and the attributes of the response body are as follows:</li>
</ul>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Meaning</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>logId</code></td>
<td><code>string</code></td>
<td>The UUID of the request.</td>
</tr>
<tr>
<td><code>errorCode</code></td>
<td><code>integer</code></td>
<td>Error code. Fixed as <code>0</code>.</td>
</tr>
<tr>
<td><code>errorMsg</code></td>
<td><code>string</code></td>
<td>Error message. Fixed as <code>"Success"</code>.</td>
</tr>
<tr>
<td><code>result</code></td>
<td><code>object</code></td>
<td>The result of the operation.</td>
</tr>
</tbody>
</table>
<ul>
<li>When the request is not processed successfully, the attributes of the response body are as follows:</li>
</ul>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Meaning</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>logId</code></td>
<td><code>string</code></td>
<td>The UUID of the request.</td>
</tr>
<tr>
<td><code>errorCode</code></td>
<td><code>integer</code></td>
<td>Error code. Same as the response status code.</td>
</tr>
<tr>
<td><code>errorMsg</code></td>
<td><code>string</code></td>
<td>Error message.</td>
</tr>
</tbody>
</table>
<p>The main operations provided by the service are as follows:</p>
<ul>
<li><b><code>infer</code></b></li>
</ul>
<p>Locate and recognize tables in the image.</p>
<p><code>POST /table-recognition</code></p>
<ul>
<li>The attributes of the request body are as follows:</li>
</ul>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Meaning</th>
<th>Required</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>file</code></td>
<td><code>string</code></td>
<td>The URL of a server-accessible image or PDF file, or the Base64-encoded content of such files. By default, for PDF files exceeding 10 pages, only the first 10 pages will be processed.<br />
To remove the page limit, please add the following configuration to the pipeline configuration file:
<pre><code>Serving:
  extra:
    max_num_input_imgs: null
</code></pre></td>
<td>Yes</td>
</tr>
<tr>
<td><code>fileType</code></td>
<td><code>integer</code> | <code>null</code></td>
<td>The type of file. <code>0</code> indicates a PDF file, <code>1</code> indicates an image file. If this attribute is not present in the request body, the file type will be inferred from the URL.</td>
<td>No</td>
</tr>
<tr>
<td><code>useDocOrientationClassify</code></td>
<td><code>boolean</code> | <code>null</code></td>
<td>Please refer to the description of the <code>use_doc_orientation_classify</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useDocUnwarping</code></td>
<td><code>boolean</code> | <code>null</code></td>
<td>Please refer to the description of the <code>use_doc_unwarping</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useLayoutDetection</code></td>
<td><code>boolean</code> | <code>null</code></td>
<td>Please refer to the description of the <code>use_layout_detection</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useOcrModel</code></td>
<td><code>boolean</code> | <code>null</code></td>
<td>Please refer to the description of the <code>use_ocr_model</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>layoutNms</code></td>
<td><code>boolean</code> | <code>null</code></td>
<td>Please refer to the description of the <code>layout_nms</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>layoutUnclipRatio</code></td>
<td><code>number</code> | <code>array</code> | <code>null</code></td>
<td>Please refer to the description of the <code>layout_unclip_ratio</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>layoutMergeBboxesMode</code></td>
<td><code>string</code> | <code>null</code></td>
<td>Please refer to the description of the <code>layout_merge_bboxes_mode</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textDetLimitSideLen</code></td>
<td><code>integer</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_det_limit_side_len</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textDetLimitType</code></td>
<td><code>string</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_det_limit_type</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textDetThresh</code></td>
<td><code>number</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_det_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textDetBoxThresh</code></td>
<td><code>number</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_det_box_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textDetUnclipRatio</code></td>
<td><code>number</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_det_unclip_ratio</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>textRecScoreThresh</code></td>
<td><code>number</code> | <code>null</code></td>
<td>Please refer to the description of the <code>text_rec_score_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useE2eWiredTableRecModel</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_e2e_wired_table_rec_model</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useE2eWirelessTableRecModel</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_e2e_wireless_table_rec_model</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useWiredTableCellsTransToHtml</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_wired_table_cells_trans_to_html</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useWirelessTableCellsTransToHtml</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_wireless_table_cells_trans_to_html</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useTableOrientationClassify</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_table_orientation_classify</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
<tr>
<td><code>useOcrResultsWithTableCells</code></td>
<td><code>boolean</code></td>
<td>Please refer to the description of the <code>use_ocr_results_with_table_cells</code> parameter of the pipeline object's <code>predict</code> method.</td>
<td>No</td>
</tr>
</tbody>
</table>
<ul>
<li>When the request is processed successfully, the <code>result</code> in the response body has the following properties:</li>
</ul>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Meaning</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>tableRecResults</code></td>
<td><code>object</code></td>
<td>The table recognition results. The array length is 1 (for image input) or the actual number of document pages processed (for PDF input). For PDF input, each element in the array represents the result of each page actually processed in the PDF file.</td>
</tr>
<tr>
<td><code>dataInfo</code></td>
<td><code>object</code></td>
<td>Information about the input data.</td>
</tr>
</tbody>
</table>
<p>Each element in <code>tableRecResults</code> is an <code>object</code> with the following properties:</p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>prunedResult</code></td>
<td><code>object</code></td>
<td>A simplified version of the <code>res</code> field in the JSON representation of the result generated by the pipeline object's <code>predict</code> method, excluding the <code>input_path</code> and the <code>page_index</code> fields.</td>
</tr>
<tr>
<td><code>outputImages</code></td>
<td><code>object</code> | <code>null</code></td>
<td>Refer to the <code>img</code> property description of the pipeline prediction results. The images are in JPEG format and are Base64-encoded.</td>
</tr>
<tr>
<td><code>inputImage</code></td>
<td><code>string</code> | <code>null</code></td>
<td>The input image. The image is in JPEG format and is Base64-encoded.</td>
</tr>
</tbody>
</table>
</details>
<details><summary>Multi-language Service Invocation Example</summary>
<details>
<summary>Python</summary>

<pre><code class="language-python">import base64
import requests

API_URL = "http://localhost:8080/table-recognition"
file_path = "./demo.jpg"

with open(file_path, "rb") as file:
    file_bytes = file.read()
    file_data = base64.b64encode(file_bytes).decode("ascii")

payload = {"file": file_data, "fileType": 1}

response = requests.post(API_URL, json=payload)

assert response.status_code == 200
result = response.json()["result"]
for i, res in enumerate(result["tableRecResults"]):
    print(res["prunedResult"])
    for img_name, img in res["outputImages"].items():
        img_path = f"{img_name}_{i}.jpg"
        with open(img_path, "wb") as f:
            f.write(base64.b64decode(img))
        print(f"Output image saved at {img_path}")
</code></pre></details>
</details>
<br/>

📱 <b>Edge Deployment</b>: Edge deployment is a method of placing computing and data processing capabilities directly on user devices, allowing the devices to process data without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed procedures, please refer to the [PaddleX Edge Deployment Guide](../../../pipeline_deploy/edge_deploy.en.md).
You can choose the appropriate deployment method according to your needs to integrate the model into your AI application.

## 4. Custom Development
If the default model weights provided by the General Table Recognition v2 Pipeline do not meet your requirements in terms of accuracy or speed, you can try to further <b>fine-tune</b> the existing models using <b>your own domain-specific or application data</b> to improve the recognition performance of the General Table Recognition v2 Pipeline in your specific scenario.

### 4.1 Model Fine-Tuning
Since the General Table Recognition v2 Pipeline consists of several modules, if the overall performance is not satisfactory, the issue may lie in any one of these modules. You can analyze the images with poor recognition results to identify which module is problematic and refer to the corresponding fine-tuning tutorial links in the table below.

<table>
<thead>
<tr>
<th>Scenario</th>
<th>Module to Fine-Tune</th>
<th>Reference Link</th>
</tr>
</thead>
<tbody>
<tr>
<td>Table classification error</td>
<td>Table Classification Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_classification.html">Link</a></td>
</tr>
<tr>
<td>Table cell positioning error</td>
<td>Table Cell Detection Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_cells_detection.html">Link</a></td>
</tr>
<tr>
<td>Table structure recognition error</td>
<td>Table Structure Recognition Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_structure_recognition.html">Link</a></td>
</tr>
<tr>
<td>Failed to detect table area</td>
<td>Layout Area Detection Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html">Link</a></td>
</tr>
<tr>
<td>Text detection omission</td>
<td>Text Detection Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_detection.html">Link</a></td>
</tr>
<tr>
<td>Inaccurate text content</td>
<td>Text Recognition Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_recognition.html">Link</a></td>
</tr>
<tr>
<td>Inaccurate image rotation correction</td>
<td>Document Image Orientation Classification Module</td>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html">Link</a></td>
</tr>
<tr>
<td>Inaccurate image distortion correction</td>
<td>Text Image Correction Module</td>
<td>Not supported for fine-tuning</td>
</tr>
</tbody>
</table>

### 4.2 Model Application
After fine-tuning with your private dataset, you will obtain a local model weight file.

If you need to use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the local path of the fine-tuned model weights to the corresponding position in the pipeline configuration file.

```yaml
SubModules:
  LayoutDetection:
    module_name: layout_detection
    model_name: PicoDet_layout_1x_table
    model_dir: null

  TableClassification:
    module_name: table_classification
    model_name: PP-LCNet_x1_0_table_cls
    model_dir: null

  WiredTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANeXt_wired
    model_dir: null

  WirelessTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANeXt_wireless
    model_dir: null

  WiredTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wired_table_cell_det
    model_dir: null

  WirelessTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wireless_table_cell_det
    model_dir: null

SubPipelines:
  DocPreprocessor:
    pipeline_name: doc_preprocessor
    use_doc_orientation_classify: True
    use_doc_unwarping: True
    SubModules:
      DocOrientationClassify:
        module_name: doc_text_orientation
        model_name: PP-LCNet_x1_0_doc_ori
        model_dir: null

      DocUnwarping:
        module_name: image_unwarping
        model_name: UVDoc
        model_dir: null

  GeneralOCR:
    pipeline_name: OCR
    text_type: general
    use_doc_preprocessor: False
    use_textline_orientation: False
    SubModules:
      TextDetection:
        module_name: text_detection
        model_name: PP-OCRv5_server_det
        model_dir: null
        limit_side_len: 960
        limit_type: max
        max_side_limit: 4000
        thresh: 0.3
        box_thresh: 0.4
        unclip_ratio: 1.5

      TextRecognition:
        module_name: text_recognition
        model_name: PP-OCRv5_server_rec
        model_dir: null
        batch_size: 1
        score_thresh: 0
```

Subsequently, refer to the command-line method or Python script method in [2.2 Local Experience](#22-Local-Experience) to load the modified pipeline configuration file.

## 5. Support for Multiple Hardware Devices
PaddleX supports a variety of mainstream hardware devices including NVIDIA GPU, Kunlunxin XPU, Ascend NPU, and Cambricon MLU. <b>Simply modify the `--device` parameter</b> to seamlessly switch between different hardware devices.

For example, if you use Ascend NPU for OCR pipeline inference, the CLI command is:

```bash
paddlex --pipeline table_recognition_v2 \
        --use_doc_orientation_classify=False \
        --use_doc_unwarping=False \
        --input table_recognition_v2.jpg \
        --save_path ./output \
        --device npu:0
```

If you want to use the General Table Recognition v2 Pipeline on a wider variety of hardware, please refer to the [PaddleX Multi-Hardware Usage Guide](../../../other_devices_support/multi_devices_use_guide.en.md).

If you want to use the Universal Table Recognition Pipeline v2 on a wider range of hardware, please refer to the [PaddleX Multi-Device Usage Guide](../../../other_devices_support/multi_devices_use_guide.en.md).
