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General Table Recognition v2 Pipeline User Guide

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 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.

❗ The General Table Recognition v2 Pipeline is still being optimized and the final version will be released in the next version of PaddleX. In order to maintain the stability of use, you can use the General Table Recognition Pipeline for table processing first, and we will release a notice when the final version of v2 is open-sourced, so please stay tuned!

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.

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.

👉Model List Details

Table Recognition Module Models:

Model	Model Download Link	Accuracy (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Description
SLANeXt_wired	Inference Model/Training Model	69.65	--	--	351M	The SLANeXt series is a new generation of table structure recognition models developed by the Baidu PaddlePaddle Vision Team. Compared to SLANet and SLANet_plus, SLANeXt focuses on recognizing table structures and has trained dedicated weights for both wired and wireless tables, significantly improving the recognition capabilities for all types of tables, especially for wired tables.
SLANeXt_wireless	Inference Model/Training Model	69.65	--	--	351M		63.69	522.536	1845.37	6.9 M

Table Classification Module Models:

Model	Model Download Link	Top1 Acc(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)
PP-LCNet_x1_0_table_cls	Inference Model/Training Model	94.2	2.35 / 0.47	4.03 / 1.35	6.6M

Table Cell Detection Module Models:

Model	Model Download Link	mAP(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
RT-DETR-L_wired_table_cell_det	Inference Model/Training Model	82.7	35.00 / 10.45	495.51 / 495.51	124M	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.
RT-DETR-L_wireless_table_cell_det	Inference Model/Training Model	82.7	35.00 / 10.45	495.51 / 495.51	124M

Text Detection Module Models:

Model	Model Download Link	Detection Hmean (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
PP-OCRv4_server_det	Inference Model/Training Model	82.69	83.34 / 80.91	442.58 / 442.58	109	The server-side text detection model of PP-OCRv4, with higher accuracy, suitable for deployment on high-performance servers.
PP-OCRv4_mobile_det	Inference Model/Training Model	77.79	8.79 / 3.13	51.00 / 28.58	4.7	The mobile text detection model of PP-OCRv4, with higher efficiency, suitable for deployment on edge devices.

Text Recognition Module Models:

Model	Model Download Link	Recognition Avg Accuracy(%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
PP-OCRv4_server_rec_doc	Inference Model/Training Model	81.53	6.65 / 2.38	32.92 / 32.92	74.7 M	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.
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.74	4.82 / 1.20	16.74 / 4.64	10.6 M	The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.
PP-OCRv4_server_rec	Inference Model/Training Model	80.61	6.58 / 2.43	33.17 / 33.17	71.2 M	The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.
en_PP-OCRv4_mobile_rec	Inference Model/Training Model	70.39	4.81 / 0.75	16.10 / 5.31	6.8 M	The ultra-lightweight English recognition model, trained based on the PP-OCRv4 recognition model, supports the recognition of English letters and numbers.

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

👉Model List Details

* Chinese Recognition Model

Model	Model Download Link	Recognition Avg Accuracy(%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
PP-OCRv4_server_rec_doc	Inference Model/Training Model	81.53	6.65 / 2.38	32.92 / 32.92	74.7 M	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.
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.74	4.82 / 1.20	16.74 / 4.64	10.6 M	The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.
PP-OCRv4_server_rec	Inference Model/Trained Model	80.61	6.58 / 2.43	33.17 / 33.17	71.2 M	The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.
PP-OCRv3_mobile_rec	Inference Model/Training Model	72.96	5.87 / 1.19	9.07 / 4.28	9.2 M	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.

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
ch_SVTRv2_rec	Inference Model/Training Model	68.81	8.08 / 2.74	50.17 / 42.50	73.9 M	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.

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
ch_RepSVTR_rec	Inference Model/Training Model	65.07	5.93 / 1.62	20.73 / 7.32	22.1 M	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.

* English Recognition Model

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
en_PP-OCRv4_mobile_rec	Inference Model/Training Model	70.39	4.81 / 0.75	16.10 / 5.31	6.8 M	The ultra-lightweight English recognition model trained based on the PP-OCRv4 recognition model supports the recognition of English and numbers.
en_PP-OCRv3_mobile_rec	Inference Model/Training Model	70.69	5.44 / 0.75	8.65 / 5.57	7.8 M	The ultra-lightweight English recognition model trained based on the PP-OCRv3 recognition model supports the recognition of English and numbers.

* Multilingual Recognition Model

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
korean_PP-OCRv3_mobile_rec	Inference Model/Training Model	60.21	5.40 / 0.97	9.11 / 4.05	8.6 M	The ultra-lightweight Korean recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Korean and numbers.
japan_PP-OCRv3_mobile_rec	Inference Model/Training Model	45.69	5.70 / 1.02	8.48 / 4.07	8.8 M	The ultra-lightweight Japanese recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Japanese and numbers.
chinese_cht_PP-OCRv3_mobile_rec	Inference Model/Training Model	82.06	5.90 / 1.28	9.28 / 4.34	9.7 M	The ultra-lightweight Traditional Chinese recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Traditional Chinese and numbers.
te_PP-OCRv3_mobile_rec	Inference Model/Training Model	95.88	5.42 / 0.82	8.10 / 6.91	7.8 M	The ultra-lightweight Telugu recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Telugu and numbers.
ka_PP-OCRv3_mobile_rec	Inference Model/Training Model	96.96	5.25 / 0.79	9.09 / 3.86	8.0 M	The ultra-lightweight Kannada recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Kannada and numbers.
ta_PP-OCRv3_mobile_rec	Inference Model/Training Model	76.83	5.23 / 0.75	10.13 / 4.30	8.0 M	The ultra-lightweight Tamil recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Tamil and numbers.
latin_PP-OCRv3_mobile_rec	Inference Model/Training Model	76.93	5.20 / 0.79	8.83 / 7.15	7.8 M	The ultra-lightweight Latin recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Latin script and numbers.
arabic_PP-OCRv3_mobile_rec	Inference Model/Training Model	73.55	5.35 / 0.79	8.80 / 4.56	7.8 M	The ultra-lightweight Arabic script recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Arabic script and numbers.
cyrillic_PP-OCRv3_mobile_rec	Inference Model/Training Model	94.28	5.23 / 0.76	8.89 / 3.88	7.9 M	The ultra-lightweight cyrillic alphabet recognition model trained based on the PP-OCRv3 recognition model supports the recognition of cyrillic letters and numbers.
devanagari_PP-OCRv3_mobile_rec	Inference Model/Training Model	96.44	5.22 / 0.79	8.56 / 4.06	7.9 M	The ultra-lightweight Devanagari script recognition model trained based on the PP-OCRv3 recognition model supports the recognition of Devanagari script and numbers.

Layout Region Detection Module Models (Optional):

Model	Model Download Link	mAP(0.5) (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Description
PP-DocLayout-L	Inference Model/Training Model	90.4	34.6244 / 10.3945	510.57 / -	123.76 M	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.
PP-DocLayout-M	Inference Model/Training Model	75.2	13.3259 / 4.8685	44.0680 / 44.0680	22.578	A balanced precision and 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-L.
PP-DocLayout-S	Inference Model/Training Model	70.9	8.3008 / 2.3794	10.0623 / 9.9296	4.834	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.

> ❗ The above list includes the 3 core models that are the focus of the layout detection module. The module supports a total of 11 full models, including multiple predefined models with different categories. The complete list of models is as follows:

👉Model List Details

* Table Layout Detection Models

Model	Model Download Link	mAP(0.5) (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Size (M)	Description
PicoDet_layout_1x_table	Inference Model/Training Model	97.5	8.02 / 3.09	23.70 / 20.41	7.4 M	A high-efficiency layout region localization model trained on a self-built dataset using PicoDet-1x, capable of locating 1 type of region: tables

* 3-category layout detection model, including tables, images, and seals

Model	Model Download Link	mAP(0.5) (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Size (M)	Description
PicoDet-S_layout_3cls	Inference Model/Training Model	88.2	8.99 / 2.22	16.11 / 8.73	4.8	A high-efficiency layout region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using the lightweight PicoDet-S model
PicoDet-L_layout_3cls	Inference Model/Training Model	89.0	13.05 / 4.50	41.30 / 41.30	22.6	A layout region localization model with balanced efficiency and accuracy, trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L
RT-DETR-H_layout_3cls	Inference Model/Training Model	95.8	114.93 / 27.71	947.56 / 947.56	470.1	A high-precision layout region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H

* 5-category English document region detection model, including text, title, table, image, and list

Model	Model Download Link	mAP(0.5) (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Size (M)	Description
PicoDet_layout_1x	Inference Model/Training Model	97.8	9.03 / 3.10	25.82 / 20.70	7.4	A high-efficiency English document layout region localization model trained on the PubLayNet dataset using PicoDet-1x

* 17-category region detection model, including 17 common layout categories: paragraph title, image, text, number, abstract, content, figure title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal

Model	Model Download Link	mAP(0.5) (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Size (M)	Description
PicoDet-S_layout_17cls	Inference Model/Training Model	87.4	9.11 / 2.12	15.42 / 9.12	4.8	A high-efficiency layout region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using the lightweight PicoDet-S model
PicoDet-L_layout_17cls	Inference Model/Training Model	89.0	13.50 / 4.69	43.32 / 43.32	22.6	A layout region localization model with balanced efficiency and accuracy, trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L
RT-DETR-H_layout_17cls	Inference Model/Training Model	98.3	115.29 / 104.09	995.27 / 995.27	470.2	A high-precision layout region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H

Text Image Correction Module Model (Optional):

Model	Model Download Link	MS-SSIM (%)	Model Storage Size (M)	Description
UVDoc	Inference Model/Training Model	54.40	30.3 M	High-precision text image correction model

The accuracy metrics of the model are measured from the DocUNet benchmark.

Document Image Orientation Classification Module Model (Optional):

Model	Model Download Link	Top-1 Acc (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Description
PP-LCNet_x1_0_doc_ori	Inference Model/Training Model	99.06	2.31 / 0.43	3.37 / 1.27	7	Document image classification model based on PP-LCNet_x1_0, containing four categories: 0 degrees, 90 degrees, 180 degrees, 270 degrees

**Test Environment Description**: - **Performance Test Environment** - **Test Dataset**: - Document Image Orientation Classification Model: A self-built dataset using PaddleX, covering various scenarios such as ID cards and documents, containing 1000 images. - Layout Region Detection Model: A self-built layout region detection dataset using PaddleOCR, including 500 images of common document types such as Chinese and English papers, magazines, contracts, books, exam papers, and research reports. - Table Layout Detection Model: A self-built table region detection dataset using PaddleOCR, containing 7835 images of paper documents with tables in both Chinese and English. - 3-Category Layout Detection Model: A self-built layout region detection dataset using PaddleOCR, including 1154 images of common document types such as Chinese and English papers, magazines, and research reports. - 5-Category English Document Region Detection Model: The evaluation dataset from [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet), containing 11245 images of English documents. - 17-Category Region Detection Model: A self-built layout region detection dataset using PaddleOCR, including 892 images of common document types such as Chinese and English papers, magazines, and research reports. - Table Structure Recognition Model: A self-built high-difficulty Chinese table recognition dataset using PaddleX. - Table Cell Detection Model: A self-built evaluation dataset using PaddleX. - Table Classification Model: A self-built evaluation dataset using PaddleX. - Text Detection Model: A self-built Chinese dataset using PaddleOCR, covering various scenarios such as street scenes, web images, documents, and handwriting, with 500 images for detection. - Chinese Recognition Model: A self-built Chinese dataset using PaddleOCR, covering various scenarios such as street scenes, web images, documents, and handwriting, with 11,000 images for text recognition. - ch_SVTRv2_rec: The A-rank evaluation set from the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task. - ch_RepSVTR_rec: The B-rank evaluation set from the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task. - English Recognition Model: A self-built English dataset using PaddleX. - Multilingual Recognition Model: A self-built multi-language dataset using PaddleX. - **Hardware Configuration**: - GPU: NVIDIA Tesla T4 - CPU: Intel Xeon Gold 6271C @ 2.60GHz - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2 - **Inference Mode Description** | Mode | GPU Configuration | CPU Configuration | Acceleration Technology Combination | |-------------|----------------------------------------|-------------------|---------------------------------------------------| | Normal Mode | FP32 Precision / No TRT Acceleration | FP32 Precision / 8 Threads | PaddleInference | | High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |

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.

2.1 Command Line Experience

You can quickly experience the effect of the table recognition pipeline with one command. Use the test file, and replace --input with the local path for prediction.

paddlex --pipeline table_recognition_v2 \
        --input table_recognition.jpg \
        --save_path ./output \
        --device gpu:0

The relevant parameter descriptions can be referred to in the 2.2 Integration via Python Script for parameter descriptions.

👉 After running, the result is: (Click to expand)

```bash {'res': {'input_path': 'table_recognition.jpg', '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': 0, 'label': 'Table', 'score': 0.9922188520431519, 'coordinate': [3.0127392, 0.14648987, 547.5102, 127.72023]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': [array([[234, 6], [316, 6], [316, 25], [234, 25]], dtype=int16), array([[38, 39], [73, 39], [73, 57], [38, 57]], dtype=int16), array([[122, 32], [201, 32], [201, 58], [122, 58]], dtype=int16), array([[227, 34], [346, 34], [346, 57], [227, 57]], dtype=int16), array([[351, 34], [391, 34], [391, 58], [351, 58]], dtype=int16), array([[417, 35], [534, 35], [534, 58], [417, 58]], dtype=int16), array([[34, 70], [78, 70], [78, 90], [34, 90]], dtype=int16), array([[287, 70], [328, 70], [328, 90], [287, 90]], dtype=int16), array([[454, 69], [496, 69], [496, 90], [454, 90]], dtype=int16), array([[ 17, 101], [ 95, 101], [ 95, 124], [ 17, 124]], dtype=int16), array([[144, 101], [178, 101], [178, 122], [144, 122]], dtype=int16), array([[278, 101], [338, 101], [338, 124], [278, 124]], dtype=int16), array([[448, 101], [503, 101], [503, 121], [448, 121]], 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': [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 'text_rec_score_thresh': 0, '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_polys': [array([[234, 6], [316, 6], [316, 25], [234, 25]], dtype=int16), array([[38, 39], [73, 39], [73, 57], [38, 57]], dtype=int16), array([[122, 32], [201, 32], [201, 58], [122, 58]], dtype=int16), array([[227, 34], [346, 34], [346, 57], [227, 57]], dtype=int16), array([[351, 34], [391, 34], [391, 58], [351, 58]], dtype=int16), array([[417, 35], [534, 35], [534, 58], [417, 58]], dtype=int16), array([[34, 70], [78, 70], [78, 90], [34, 90]], dtype=int16), array([[287, 70], [328, 70], [328, 90], [287, 90]], dtype=int16), array([[454, 69], [496, 69], [496, 90], [454, 90]], dtype=int16), array([[ 17, 101], [ 95, 101], [ 95, 124], [ 17, 124]], dtype=int16), array([[144, 101], [178, 101], [178, 122], [144, 122]], dtype=int16), array([[278, 101], [338, 101], [338, 124], [278, 124]], dtype=int16), array([[448, 101], [503, 101], [503, 121], [448, 121]], dtype=int16)], 'rec_boxes': array([[234, 6, 316, 25], [ 38, 39, 73, 57], [122, 32, 201, 58], [227, 34, 346, 57], [351, 34, 391, 58], [417, 35, 534, 58], [ 34, 70, 78, 90], [287, 70, 328, 90], [454, 69, 496, 90], [ 17, 101, 95, 124], [144, 101, 178, 122], [278, 101, 338, 124], [448, 101, 503, 121]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([3.18822289e+00, 1.46489874e-01, 5.46996138e+02, 3.08782365e+01]), array([ 3.21032453, 31.1510637 , 110.20750237, 65.14108063]), array([110.18174553, 31.13076188, 213.00813103, 65.02860047]), array([212.96108818, 31.09959008, 404.19618034, 64.99535157]), array([404.08112907, 31.18304802, 547.00864983, 65.0847223 ]), array([ 3.21772957, 65.0738733 , 110.33685875, 96.07921387]), array([110.23703575, 65.02486207, 213.08839226, 96.01378419]), array([213.06095695, 64.96230103, 404.28425407, 95.97141816]), array([404.23704338, 65.04879548, 547.01273918, 96.03654267]), array([ 3.22793937, 96.08334137, 110.38572502, 127.08698823]), array([110.40586662, 96.10539795, 213.19943047, 127.07002045]), array([213.12627983, 96.0539148 , 404.42686272, 127.02842499]), array([404.33042717, 96.07251526, 547.01273918, 126.45088746])], 'pred_html': '

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

', 'table_ocr_pred': {'rec_polys': [array([[234, 6], [316, 6], [316, 25], [234, 25]], dtype=int16), array([[38, 39], [73, 39], [73, 57], [38, 57]], dtype=int16), array([[122, 32], [201, 32], [201, 58], [122, 58]], dtype=int16), array([[227, 34], [346, 34], [346, 57], [227, 57]], dtype=int16), array([[351, 34], [391, 34], [391, 58], [351, 58]], dtype=int16), array([[417, 35], [534, 35], [534, 58], [417, 58]], dtype=int16), array([[34, 70], [78, 70], [78, 90], [34, 90]], dtype=int16), array([[287, 70], [328, 70], [328, 90], [287, 90]], dtype=int16), array([[454, 69], [496, 69], [496, 90], [454, 90]], dtype=int16), array([[ 17, 101], [ 95, 101], [ 95, 124], [ 17, 124]], dtype=int16), array([[144, 101], [178, 101], [178, 122], [144, 122]], dtype=int16), array([[278, 101], [338, 101], [338, 124], [278, 124]], dtype=int16), array([[448, 101], [503, 101], [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)]}}]}} ``` The explanation of the running result parameters can refer to the result interpretation in [2.2.2 Python Script Integration](#222-python-script-integration). The visualization results are saved under `save_path`, where the visualization result of table recognition is as follows:

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:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="table_recognition_v2")

output = pipeline.predict(
input="table_recognition.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:

Parameter	Description	Type	Default Value
`pipeline`	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.	`str`	`None`
`config`	Specific configuration information for the pipeline (if set simultaneously with `pipeline`, it has higher priority than `pipeline`, and the pipeline name must be consistent with `pipeline`).	`dict[str, Any]`	`None`
`device`	The device used for pipeline inference. It supports specifying specific GPU card numbers, such as "gpu:0", specific card numbers for other hardware, such as "npu:0", or CPU as "cpu".	`str`	`gpu:0`
`use_hpip`	Whether to enable high-performance inference. This is only available if the pipeline supports high-performance inference.	`bool`	`False`

(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:

Parameter	Description	Type	Options	Default Value
`input`	Data to be predicted, supports multiple input types, required	`Python Var\|str\|list`	Python Var: Such as `numpy.ndarray` representing image data str: Such as the local path of an image file or PDF file: `/root/data/img.jpg`; such as URL link, such as the network URL of an image file or PDF file: Example; such as local directory, the directory must contain the images to be predicted, such as the local path: `/root/data/` (currently does not support prediction of PDF files in the directory, PDF files need to be specified to a specific file path) List: The list elements must be the above types of data, such as `[numpy.ndarray, numpy.ndarray]`, `["/root/data/img1.jpg", "/root/data/img2.jpg"]`, `["/root/data1", "/root/data2"]`	`None`
`device`	Pipeline inference device	`str\|None`	CPU: Such as `cpu` indicating using CPU for inference; GPU: Such as `gpu:0` indicating using the 1st GPU for inference; NPU: Such as `npu:0` indicating using the 1st NPU for inference; XPU: Such as `xpu:0` indicating using the 1st XPU for inference; MLU: Such as `mlu:0` indicating using the 1st MLU for inference; DCU: Such as `dcu:0` indicating using the 1st DCU for inference; None: If set to `None`, it will default to using the parameter value initialized by the pipeline. During initialization, it will preferentially use the local GPU 0 device, if not available, it will use the CPU device;	`None`
`use_doc_orientation_classify`	Whether to use the document orientation classification module	`bool\|None`	bool: `True` or `False`; None: If set to `None`, it will default to using the parameter value initialized by the pipeline, initialized to `True`;	`None`
`use_doc_unwarping`	Whether to use the document unwarping module	`bool\|None`	bool: `True` or `False`; None: If set to `None`, it will default to using the parameter value initialized by the pipeline, initialized to `True`;	`None`
`text_det_limit_side_len`	Image side length limit for text detection	`int\|None`	int: Any integer greater than `0`; None: If set to `None`, it will default to using the parameter value initialized by the pipeline, initialized to `960`;	`None`	`text_det_limit_type`	Image side length limit type for text detection	`str\|None`	str: Supports `min` and `max`, `min` indicates ensuring the shortest side of the image is not less than `det_limit_side_len`, `max` indicates ensuring the longest side of the image is not greater than `limit_side_len` None: If set to `None`, it will default to using the parameter value initialized by the pipeline, initialized to `max`;	`None`	`text_det_thresh`	Detection pixel threshold, only pixels with scores greater than this threshold in the output probability map will be considered as text pixels	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to using the parameter value initialized by the pipeline `0.3`	`None`	`text_det_box_thresh`	Detection box threshold, the result will be considered as a text region if the average score of all pixels within the detection box is greater than this threshold	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to using the parameter value initialized by the pipeline `0.6`	`None`	`text_det_unclip_ratio`	Text detection expansion ratio, this method is used to expand the text region, the larger the value, the larger the expanded area	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to using the parameter value initialized by the pipeline `2.0`	`None`	`text_rec_score_thresh`	Text recognition threshold, text results with scores greater than this threshold will be retained	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to using the parameter value initialized by the pipeline `0.0`, meaning no threshold	`None`
`use_layout_detection`	Whether to use the layout detection module	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized by the pipeline will be used, initialized as `True`;	`None`
`layout_threshold`	Confidence threshold for layout detection; only scores above this threshold will be output	`float\|dict\|None`	float: Any floating-point number greater than `0` dict: Key is an integer category ID, value is any floating-point number greater than `0` None: If set to `None`, the default value initialized by the pipeline will be used, initialized as `0.5`	`None`
`layout_nms`	Whether to use NMS post-processing for layout detection	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized by the pipeline will be used, initialized as `True`;	`None`
`layout_unclip_ratio`	Scale factor for the side length of detection boxes; if not specified, the default PaddleX official model configuration will be used	`float\|list\|None`	float: A floating-point number greater than 0, e.g., 1.1, indicating that the center of the detection box remains unchanged, and both the width and height are scaled by 1.1 times list: e.g., [1.2, 1.5], indicating that the center of the detection box remains unchanged, the width is scaled by 1.2 times, and the height is scaled by 1.5 times None: If set to `None`, the default value initialized by the pipeline will be used, initialized as 1.0
`layout_merge_bboxes_mode`	Merging mode for detection boxes output by the model; if not specified, the default PaddleX official model configuration will be used	`string\|None`	large: When set to large, only the outermost box will be retained for overlapping detection boxes, and the inner overlapping boxes will be removed. small: When set to small, only the innermost boxes will be retained for overlapping detection boxes, and the outer overlapping boxes will be removed. union: No filtering of boxes will be performed; both inner and outer boxes will be retained. None: If set to `None`, the default value initialized by the pipeline will be used, initialized as `large`	None

(3) Process the prediction results. The prediction result for each sample is of type dict, 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:

Method	Description	Parameter	Type	Parameter Description	Default Value
`print()`	Print the result to the terminal	`format_json`	`bool`	Whether to format the output content using `JSON` indentation	`True`
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data, making it more readable. This is only effective when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Control whether non-`ASCII` characters are escaped to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` retains the original characters. This is only effective when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a JSON file	`save_path`	`str`	The file path for saving. When it is a directory, the saved file name will match the input file name	N/A
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data, making it more readable. This is only effective when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Control whether non-`ASCII` characters are escaped to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` retains the original characters. This is only effective when `format_json` is `True`	`False`
`save_to_img()`	Save the result as an image file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	N/A
`save_to_xlsx()`	Save the result as an xlsx file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	N/A
`save_to_html()`	Save the result as an HTML file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	N/A

Calling the print() method will print the result to the terminal, with the printed content explained as follows:
- input_path: (str) The input path of the image to be predicted
- model_settings: (Dict[str, bool]) Configuration parameters required for the pipeline model
  - use_doc_preprocessor: (bool) Controls whether to enable the document preprocessing sub-pipeline
- use_layout_detection: (bool) Controls whether to enable the layout detection sub-pipeline
- use_ocr_model: (bool) Controls whether to enable the OCR sub-pipeline
- layout_det_res: (Dict[str, Union[List[numpy.ndarray], List[float]]]) Output result 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, saved as None when the input is a numpy.ndarray
  - page_index: (Union[int, None]) If the input is a PDF file, it indicates the current page number of the PDF, otherwise it is None
  - boxes: (List[Dict]) List of detection boxes for layout seal regions, each element in the list contains the following fields
    - cls_id: (int) The class ID of the detection box
    - score: (float) The confidence score of the detection box
    - coordinate: (List[float]) The coordinates of the four corners of the detection 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]]) The output result of the document preprocessing sub-pipeline. Exists only when use_doc_preprocessor=True
  - input_path: (Union[str, None]) The image path accepted by the image preprocessing sub-pipeline, saved as None when the input is numpy.ndarray
  - model_settings: (Dict) Model configuration parameters for the preprocessing sub-pipeline
    - 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 prediction result of document orientation classification. When enabled, the values are [0,1,2,3], corresponding to [0°,90°,180°,270°] respectively; when not enabled, it is -1
- dt_polys: (List[numpy.ndarray]) List of polygons for text detection. Each detection box is represented by a numpy array of 4 vertex coordinates, with the array shape being (4, 2) and the data type being int16
- dt_scores: (List[float]) 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 during image preprocessing
  - limit_type: (str) The processing method for the side length limit
  - thresh: (float) Confidence threshold for text pixel classification
  - box_thresh: (float) Confidence threshold for text detection boxes
  - unclip_ratio: (float) Expansion coefficient for text detection boxes
  - text_type: (str) Type of text detection, currently fixed as "general"
- text_rec_score_thresh: (float) Filtering threshold for text recognition results
- rec_texts: (List[str]) List of text recognition results, only includes texts with confidence scores exceeding text_rec_score_thresh
- rec_scores: (List[float]) List of confidence scores for text recognition, filtered by text_rec_score_thresh
- rec_polys: (List[numpy.ndarray]) List of text detection boxes after confidence filtering, same format as dt_polys
- rec_boxes: (numpy.ndarray) 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 rectangle, where (x_min, y_min) is the top-left coordinate and (x_max, y_max) is the bottom-right coordinate
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 also supports obtaining visualized images and prediction results through attributes, as follows:

Attribute	Attribute Description
`json`	Get the predicted `json` format result
`img`	Get the visualized image in `dict` format

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

from paddlex import create_pipeline

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

output = pipeline.predict(
    input="table_recognition.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/")

Note: 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 with development integration/deployment.

If you need to directly apply the pipeline in your Python project, you can refer to the example code in 2.2 Integration via Python Script.

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

🚀 High-Performance Inference: In actual production environments, many applications have stringent performance requirements (especially response speed) for deployment strategies to ensure efficient system operation and smooth user experience. Therefore, PaddleX provides a high-performance inference plugin designed to deeply optimize the performance of model inference and pre/post-processing, significantly speeding up the end-to-end process. For detailed high-performance inference procedures, please refer to the PaddleX High-Performance Inference Guide.

☁️ Service Deployment: Service deployment is a common form of deployment in actual production environments. By encapsulating inference functions as services, clients can access these services via network requests to obtain inference results. PaddleX supports multiple pipeline service deployment solutions. For detailed pipeline service deployment procedures, please refer to the PaddleX Service Deployment Guide.

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

API Reference

For the main operations provided by the service:

The HTTP request method is POST.
Both the request body and response body are JSON data (JSON objects).
When the request is processed successfully, the response status code is 200, and the properties of the response body are as follows:

Name	Type	Meaning
`logId`	`string`	The UUID of the request.
`errorCode`	`integer`	Error code. Fixed as `0`.
`errorMsg`	`string`	Error message. Fixed as `"Success"`.
`result`	`object`	The result of the operation.

When the request is not processed successfully, the properties of the response body are as follows:

Name	Type	Meaning
`logId`	`string`	The UUID of the request.
`errorCode`	`integer`	Error code. Same as the response status code.
`errorMsg`	`string`	Error message.

The main operations provided by the service are as follows:

infer

Locate and recognize tables in the image.

POST /table-recognition

The properties of the request body are as follows:

Name	Type	Meaning	Required
`file`	`string`	The URL of an image file or PDF file accessible by the server, or the Base64-encoded content of the above file types. For PDF files with more than 10 pages, only the content of the first 10 pages will be used.	Yes
`fileType`	`integer` \| `null`	The type of the file. `0` indicates a PDF file, and `1` indicates an image file. If this attribute is not present in the request body, the file type will be inferred based on the URL.	No
`useDocOrientationClassify`	`boolean` \| `null`	See the `use_doc_orientation_classify` parameter description in the production `predict` method.	No
`useDocUnwarping`	`boolean` \| `null`	See the `use_doc_unwarping` parameter description in the production `predict` method.	No
`useLayoutDetection`	`boolean` \| `null`	See the `use_layout_detection` parameter description in the production `predict` method.	No
`useOcrModel`	`boolean` \| `null`	See the `use_ocr_model` parameter description in the production `predict` method.	No
`layoutThreshold`	`number` \| `null`	See the `layout_threshold` parameter description in the production `predict` method.	No
`layoutNms`	`boolean` \| `null`	See the description of the `layout_nms` parameter in the production `predict` method.	No
`layoutUnclipRatio`	`number` \| `array` \| `null`	See the description of the `layout_unclip_ratio` parameter in the production `predict` method.	No
`layoutMergeBboxesMode`	`string` \| `null`	See the description of the `layout_merge_bboxes_mode` parameter in the production `predict` method.	No
`textDetLimitSideLen`	`integer` \| `null`	See the description of the `text_det_limit_side_len` parameter in the production `predict` method.	No
`textDetLimitType`	`string` \| `null`	See the description of the `text_det_limit_type` parameter in the production `predict` method.	No
`textDetThresh`	`number` \| `null`	See the description of the `text_det_thresh` parameter in the production `predict` method.	No
`textDetBoxThresh`	`number` \| `null`	See the description of the `text_det_box_thresh` parameter in the production `predict` method.	No
`textDetUnclipRatio`	`number` \| `null`	See the description of the `text_det_unclip_ratio` parameter in the production `predict` method.	No
`textRecScoreThresh`	`number` \| `null`	See the description of the `text_rec_score_thresh` parameter in the production `predict` method.	No

Each element in tableRecResults is an object with the following properties:

Name	Type	Description
`prunedResult`	`object`	A simplified version of the `res` field in the JSON representation generated by the `predict` method of the pipeline object, with the `input_path` field removed.
`outputImages`	`object` \| `null`	See the description of the `img` attribute in the result of the pipeline prediction. Images are in JPEG format and encoded with Base64.
`inputImage`	`string` \| `null`	The input image. The image is in JPEG format and encoded with Base64.

Multi-language service call example

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("Detected tables:")
print(res["tables"])
layout_img_path = f"layout_{i}.jpg"
with open(layout_img_path, "wb") as f:
    f.write(base64.b64decode(res["layoutImage"]))
ocr_img_path = f"ocr_{i}.jpg"
with open(ocr_img_path, "wb") as f:
    f.write(base64.b64decode(res["ocrImage"]))
print(f"Output images saved at {layout_img_path} and {ocr_img_path}")

📱 Edge Deployment: Edge deployment is a method of placing computing and data processing capabilities directly on user devices, allowing them to process data without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method based on your needs to integrate the model pipeline into subsequent AI applications.

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 fine-tune the existing models using your own domain-specific or application data 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.

Scenario	Fine-Tuning Module	Fine-Tuning Reference Link
Table classification errors	Table Classification Module	Link
Table cell localization errors	Table Cell Detection Module	Link
Table structure recognition errors	Table Structure Recognition Module	Link
Failure to detect table regions	Layout Region Detection Module	Link
Missing text detection	Text Detection Module	Link
Inaccurate text content	Text Recognition Module	Link
Inaccurate whole-image rotation correction	Document Image Orientation Classification Module	Link
Inaccurate image distortion correction	Text Image Correction Module	Fine-tuning not supported

4.2 Model Application

After fine-tuning with your private dataset, you can obtain the local model weight file.

To use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the local path of the fine-tuned model weights in the corresponding position in the configuration file:

SubModules:
  LayoutDetection:
    module_name: layout_detection
    model_name: PicoDet_layout_1x_table
    model_dir: null # 替换为微调后的版面区域检测模型权重路径

  TableClassification:
    module_name: table_classification
    model_name: PP-LCNet_x1_0_table_cls
    model_dir: null # 替换为微调后的表格分类模型权重路径

  WiredTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANeXt_wired
    model_dir: null # 替换为微调后的有线表格结构识别模型权重路径

  WirelessTableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANeXt_wireless
    model_dir: null # 替换为微调后的无线表格结构识别模型权重路径

  WiredTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wired_table_cell_det
    model_dir: null # 替换为微调后的有线表格单元格检测模型权重路径

  WirelessTableCellsDetection:
    module_name: table_cells_detection
    model_name: RT-DETR-L_wireless_table_cell_det
    model_dir: null # 替换为微调后的无线表格单元格检测模型权重路径

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 # 替换为微调后的文档图像方向分类模型权重路径

      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-OCRv4_server_det
        model_dir: null # 替换为微调后的文本检测模型权重路径
        limit_side_len: 960
        limit_type: max
        thresh: 0.3
        box_thresh: 0.6
        unclip_ratio: 2.0

      TextRecognition:
        module_name: text_recognition
        model_name: PP-OCRv4_server_rec
        model_dir: null # 替换为微调后文本识别的模型权重路径
        batch_size: 1
        score_thresh: 0

Subsequently, refer to the command line method or Python script method in 2.2 Local Experience to load the modified pipeline configuration file.

5. Multi-Hardware Support

PaddleX supports various mainstream hardware devices such as NVIDIA GPU, Kunlun Chip XPU, Ascend NPU, and Cambricon MLU. Simply modify the --device parameter to achieve seamless switching between different hardware.

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

paddlex --pipeline table_recognition_v2 \
        --input table_recognition.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.

table_recognition_v2.en.md 82 KB Histórico Raw

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General Table Recognition v2 Pipeline User Guide

1. Introduction to General Table Recognition v2 Pipeline

2. Quick Start

2.1 Online Experience

2.2 Local Experience

2.1 Command Line Experience

2.2 Python Script Integration

3. Development Integration/Deployment

4. Custom Development

4.1 Model Fine-Tuning

4.2 Model Application

5. Multi-Hardware Support

table_recognition_v2.en.md 82 KB

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