General Table Recognition Pipeline Tutorial¶

1. Introduction to General Table Recognition Pipeline¶

Table recognition is a technology that automatically identifies and extracts table content and 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 editable formats, facilitating further processing and analysis of data.

The General Table Recognition Pipeline is designed to solve table recognition tasks by identifying tables in images and outputting them in HTML format. This pipeline integrates the well-known SLANet and SLANet_plus table structure recognition models. Based on this pipeline, precise predictions of tables can be achieved, covering a wide range of applications in general, manufacturing, finance, transportation, and other fields. The pipeline also provides flexible service deployment options, supporting various hardware and programming languages for integration. Moreover, it offers custom development capabilities, allowing you to train and optimize models on your own dataset, which can then be seamlessly integrated.

The General Table Recognition Pipeline includes essential modules for table structure recognition, text detection, and text recognition, as well as optional modules for layout area detection, document image orientation classification, and text image correction.

If you prioritize model accuracy, choose a high-accuracy model; if you prioritize inference speed, choose a faster model; if you care about model size, choose a smaller model.

👉Model List Details

Table Structure Recognition Module Models:

Model	Model Download Link	Accuracy (%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
SLANet	Inference Model/Training Model	59.52	103.08 / 103.08	197.99 / 197.99	6.9 M	SLANet is a self-developed table structure recognition model by the Baidu PaddleX Team. This model significantly improves the accuracy and inference speed of table structure recognition by using a CPU-friendly lightweight backbone network PP-LCNet, a high-low feature fusion module CSP-PAN, and a feature decoding module SLA Head that aligns structure and position information.
SLANet_plus	Inference Model/Training Model	63.69	140.29 / 140.29	195.39 / 195.39	6.9 M

Text Detection Module Models:

Model	Model Download Link	Detection Hmean (%)	GPU 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 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, which is more efficient and suitable for deployment on edge devices.

Text Recognition Module Models:

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
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.20	4.82 / 4.82	16.74 / 4.64	10.6 M	PP-OCRv4 is the next version of the self-developed text recognition model PP-OCRv3 by the PaddlePaddle Vision Team. By introducing data augmentation and GTC-NRTR guidance branches, it further improves text recognition accuracy without changing the model inference speed. This model provides both server and mobile versions to meet industrial needs in different scenarios.
PP-OCRv4_server_rec	Inference Model/Training Model	79.20	6.58 / 6.58	33.17 / 33.17	71.2 M

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 / 8.08	50.17 / 42.50	73.9 M	SVTRv2 is a server text recognition model developed by the OpenOCR team from the Visual and Learning Laboratory (FVL) at Fudan University. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task 1: End-to-End OCR Recognition, with a 6% improvement in end-to-end recognition accuracy compared to 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 / 5.93	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 competition - Task 1: OCR end-to-end recognition task. Compared with PP-OCRv4, the end-to-end recognition accuracy on the B list was improved by 2.5%, while the inference speed remained the same.

Model	Model Download Link	mAP(0.5)（%）	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
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 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.
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.

Layout Detection Module Models (Optional):

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
PicoDet_layout_1x	Inference Model/Training Model	86.8	9.03 / 3.10	25.82 / 20.70	7.4	A high-efficiency layout detection model trained on the PubLayNet dataset based on PicoDet-1x, capable of locating five types of regions: text, title, table, image, and list.
PicoDet_layout_1x_table	Inference Model/Training Model	95.7	8.02 / 3.09	23.70 / 20.41	7.4 M	A high-efficiency layout detection model trained on a custom dataset based on PicoDet-1x, capable of locating one type of region: table.
PicoDet-S_layout_3cls	Inference Model/Training Model	87.1	8.99 / 2.22	16.11 / 8.73	4.8	A high-efficiency layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on the lightweight PicoDet-S model, including three categories: table, image, and seal.
PicoDet-S_layout_17cls	Inference Model/Training Model	70.3	9.11 / 2.12	15.42 / 9.12	4.8	A high-efficiency layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on the lightweight PicoDet-S model, including 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal.
PicoDet-L_layout_3cls	Inference Model/Training Model	89.3	13.05 / 4.50	41.30 / 41.30	22.6	A high-efficiency layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on PicoDet-L, including three categories: table, image, and seal.
PicoDet-L_layout_17cls	Inference Model/Training Model	79.9	13.50 / 4.69	43.32 / 43.32	22.6	A high-efficiency layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on PicoDet-L, including 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal.
RT-DETR-H_layout_3cls	Inference Model/Training Model	95.9	114.93 / 27.71	947.56 / 947.56	470.1	A high-precision layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on RT-DETR-H, including three categories: table, image, and seal.
RT-DETR-H_layout_17cls	Inference Model/Training Model	92.6	115.29 / 104.09	995.27 / 995.27	470.2	A high-precision layout detection model trained on a custom dataset in Chinese and English papers, magazines, and research reports based on RT-DETR-H, including 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal.

Text Image Rectification Module Models (Optional):

Model	Model Download Link	MS-SSIM (%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
UVDoc	Inference Model/Training Model	54.40	-/-	-/-	30.3 M	High-precision text image rectification model.

Document Image Orientation Classification Module Models (Optional):

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

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/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.
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.

Test Environment Description:

Performance Test Environment
- Test Dataset：
  - Document Image Orientation Classification Module: A self-built dataset using PaddleX, covering multiple scenarios such as ID cards and documents, containing 1000 images.
  - Text Image Rectification Model: DocUNet.
  - Layout Region Detection Model: A layout region analysis dataset built by PaddleOCR, containing 10,000 images of common document types such as Chinese and English papers, magazines, and research reports.
  - Table Structure Recognition Model: An internal English table recognition dataset built by PaddleX.
  - Text Detection Model: A Chinese dataset built by PaddleOCR, covering multiple scenarios such as street scenes, web images, documents, and handwriting, with 500 images for detection.
  - Chinese Recognition Model: A Chinese dataset built by PaddleOCR, covering multiple scenarios such as street scenes, web images, documents, and handwriting, with 11,000 images for text recognition.
  - ch_SVTRv2_rec: PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task A-list evaluation set.
  - ch_RepSVTR_rec: PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task B-list evaluation set.
  - English Recognition Model: An English dataset built by PaddleX.
  - Multilingual Recognition Model: A multilingual dataset built by 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¶

The pre-trained models provided by PaddleX can be quickly experienced. You can experience the effect of the general table recognition pipeline online on the Baidu AIStudio Community, or you can experience it locally using the command line or Python.

2.1 Online Experience¶

You can experience online the effect of the general table recognition pipeline using the official demo images, for example:

If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the method in Section 2.2 Local Experience for local deployment. If you are not satisfied with the effect, you can fine-tune the models in the pipeline using private data. If you have local training hardware resources, you can start training directly on your local machine; if not, the Baidu AI Studio provides a one-click training service. No code is required; simply upload your data to start the training task.

2.2 Local Experience¶

Before using the General Table Recognition pipeline locally, ensure you have installed the PaddleX wheel package following the PaddleX Local Installation Guide.

2.1 Command Line Experience¶

You can quickly experience the table recognition pipeline with a single command. Use the test file, and replace --input with your local path for prediction.

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

The content of the parameters can refer to the parameter description in 2.2 Python Script Method.

After running, the result will be printed to the terminal, as follows:

{'res': {'input_path': 'table_recognition.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': 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],
        ...,
        [234,  25]],

       ...,

       [[448, 101],
        ...,
        [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': array([-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': array([0.99512607, ..., 0.99844509]), 'rec_polys': array([[[234,   6],
        ...,
        [234,  25]],

       ...,

       [[448, 101],
        ...,
        [448, 121]]], dtype=int16), 'rec_boxes': array([[234, ...,  25],
       ...,
       [448, ..., 121]], dtype=int16)}, 'table_res_list': [{'cell_box_list': array([[  5.01273918, ...,  32.14648987],
       ...,
       [405.01273918, ..., 124.14648987]]), 'pred_html': '<html><body><table><tbody><tr><td colspan="4">CRuncover</td></tr><tr><td>Dres</td><td>连续工作3</td><td>取出来放在网上 没想</td><td>江、整江等八大</td></tr><tr><td>Abstr</td><td></td><td>rSrivi</td><td>$709.</td></tr><tr><td>cludingGiv</td><td>2.72</td><td>Ingcubic</td><td>$744.78</td></tr></tbody></table></body></html>', 'table_ocr_pred': {'rec_polys': array([[[234,   6],
        ...,
        [234,  25]],

       ...,

       [[448, 101],
        ...,
        [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': array([0.99512607, ..., 0.99844509]), 'rec_boxes': array([[234, ...,  25],
       ...,
       [448, ..., 121]], dtype=int16)}}]}}

The running result parameters can be referred to the result explanation in 2.2 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 results. Generally, in a project, it is often necessary to integrate through code. You can complete the fast inference of the pipeline with just a few lines of code. The inference code is as follows:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="table_recognition")

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 pipeline object, with the specific parameters described as follows:

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

(2) The predict() method of the general table recognition pipeline object is called to perform inference prediction. This method returns a generator. Below are the parameters and their descriptions for the predict() method:

Parameter	Description	Type	Options	Default Value
`input`	Data to be predicted, supports multiple input types, required.	`Python Var\|str\|list`	Python Var: Image data represented by `numpy.ndarray`. str: Local path of image or PDF files, e.g., `/root/data/img.jpg`; URL link, such as the network URL of an image or PDF file: Example; Local directory, the directory should contain images to be predicted, e.g., `/root/data/` (currently, prediction for PDF files in directories is not supported; PDF files must specify the exact file path). List: List elements must be of the above types, such as `[numpy.ndarray, numpy.ndarray]`, `[“/root/data/img1.jpg”, “/root/data/img2.jpg”]`, `[“/root/data1”, “/root/data2”]`.	`None`
`device`	Inference device.	`str\|None`	CPU: Use CPU for inference, e.g., `cpu`. GPU: Use the first GPU for inference, e.g., `gpu:0`. NPU: Use the first NPU for inference, e.g., `npu:0`. XPU: Use the first XPU for inference, e.g., `xpu:0`. MLU: Use the first MLU for inference, e.g., `mlu:0`. DCU: Use the first DCU for inference, e.g., `dcu:0`. None: If set to `None`, the default value initialized by the production line will be used. During initialization, the local GPU 0 will be prioritized; if unavailable, the CPU will be used.	`None`
`use_doc_orientation_classify`	Whether to use the document orientation classification module.	`bool\|None`	bool: `True` or `False`. None: If set to `None`, the default value initialized by the production line will be used, initialized as `True`.	`None`
`use_doc_unwarping`	Whether to use the document unwarping module.	`bool\|None`	bool: `True` or `False`. None: If set to `None`, the default value initialized by the production line will be used, initialized as `True`.	`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 production line will be used, initialized as `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`, the default value initialized in the production line will be used, initialized as `960`;	`None`
`text_det_limit_type`	Type of image side length limit for text detection	`str\|None`	str: Supports `min` and `max`. `min` ensures the shortest side of the image is not less than `det_limit_side_len`, `max` ensures the longest side of the image is not greater than `limit_side_len` None: If set to `None`, the default value initialized in the production line will be used, initialized as `max`;	`None`
`text_det_thresh`	Detection pixel threshold, in the output probability map, pixels with scores greater than this threshold will be considered as text pixels	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, the default value initialized in the production line will be used, initialized as `0.3`	`None`
`text_det_box_thresh`	Detection box threshold, for a detection result, if the average score of all pixels within the bounding box is greater than this threshold, it will be considered as a text area	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, the default value initialized in the production line will be used, initialized as `0.6`	`None`
`text_det_unclip_ratio`	Text detection expansion ratio, this value determines how much the text area will be expanded, the larger the value, the larger the expansion area	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, the default value initialized in the production line will be used, initialized as `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`, the default value initialized in the production line will be used, initialized as `0.0`, meaning no threshold is set	`None`
`use_table_cells_ocr_results`	Whether to enable Table-Cells-OCR mode, when not enabled, use global OCR result to fill to HTML table, when enabled, do OCR cell by cell and fill to HTML table (it will increase the time consuming). Both of them perform differently in different scenarios, please choose according to the actual situation.	`bool`	bool：`True` or `False`	`False`

(3) Process the prediction results. 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.

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 and make it more readable. Only effective when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Control whether to escape non-`ASCII` characters to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` retains the original characters. Only effective when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a JSON file	`save_path`	`str`	The path where the file will be saved. If it is a directory, the saved file name will match the input file name	None
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data and make it more readable. Only effective when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Control whether to escape non-`ASCII` characters to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` retains the original characters. Only effective when `format_json` is `True`	`False`
`save_to_img()`	Save the result as an image file	`save_path`	`str`	The path where the file will be saved, supporting both directory and file paths	None
`save_to_xlsx()`	Save the result as an xlsx file	`save_path`	`str`	The path where the file will be saved, supporting both directory and file paths	None
`save_to_html()`	Save the result as an html file	`save_path`	`str`	The path where the file will be saved, supporting both directory and file paths	None

Calling the print() method will print the result 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]) Model parameters required for the pipeline configuration
  - use_doc_preprocessor: (bool) Controls whether to enable the document preprocessor sub-line
    - doc_preprocessor_res: (Dict[str, Union[str, Dict[str, bool], int]]) Output result of the document preprocessor sub-line. It exists only when use_doc_preprocessor=True
  - input_path: (Union[str, None]) The image path accepted by the image preprocessor sub-line. When the input is numpy.ndarray, it is saved as None
  - model_settings: (Dict) Model configuration parameters for the preprocessor sub-line
    - use_doc_orientation_classify: (bool) Controls whether to enable document orientation classification
    - use_doc_unwarping: (bool) Controls whether to enable document warping correction
  - angle: (int) The prediction result of document orientation classification. When enabled, the values are [0,1,2,3], corresponding to [0°,90°,180°,270°]; when not enabled, 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 four vertex coordinates, with a shape of (4, 2) and a data type of 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 during image preprocessing
  - limit_type: (str) The processing method for the side length limit
  - 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 exceeding 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 after confidence filtering, in the same format as dt_polys
- rec_boxes: (numpy.ndarray) An array of rectangular bounding boxes for detection boxes, with a shape of (n, 4) and a dtype of int16. Each row represents the coordinates [x_min, y_min, x_max, y_max] 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 it is a directory, the saved path will be save_path/{your_img_basename}.json; if it is a file, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, the numpy.array type will be converted to a list format.
Calling the save_to_img() method will save the visualization results to the specified save_path. If it is a directory, the saved path will be save_path/{your_img_basename}_ocr_res_img.{your_img_extension}; if it is a file, it will be saved directly to that file. (The pipeline usually contains many result images, so it is not recommended to specify a specific file path directly, otherwise multiple images will be overwritten and only the last one will be retained)
Calling the save_to_html() method will save the above content to the specified save_path. If it is a directory, the saved path will be save_path/{your_img_basename}.html; if it is a file, it will be saved directly to that file. In the general table recognition 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 it is a directory, the saved path will be save_path/{your_img_basename}.xlsx; if it is a file, it will be saved directly to that file. In the general table recognition pipeline, the Excel table form of the table in the image will be written to the specified xlsx file.
In addition, it also supports obtaining visualized images with results and prediction results through attributes, as follows:

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

The prediction result obtained through the json attribute is data of the dict type, and its content is consistent with the content saved by calling the save_to_json() method.
The prediction result returned by the img attribute is data of the dictionary type. 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: the visualized image of table recognition results, the visualized image of OCR results, the visualized image of layout area detection results, and the visualized 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 configuration file for the general table recognition pipeline 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 --save_path ./my_path

If you have obtained the configuration file, you can customize the settings for the general table recognition pipeline. You just need to modify the value of the pipeline parameter 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.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 general table recognition pipeline initialization parameters, you can directly modify the parameters in the configuration file and load the configuration file for prediction. At the same time, CLI prediction also supports passing in a configuration file, and you can specify the path of the configuration file with --pipeline.

3. Development Integration/Deployment¶

If the pipeline meets your requirements for inference speed and accuracy, you can directly proceed 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 Method.

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

🚀 High-Performance Inference: In actual production environments, many applications have strict performance requirements (especially response speed) for deployment strategies 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 high-performance inference procedures, please refer to the PaddleX High-Performance Inference Guide.

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

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

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 attributes 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 attributes 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	Description	Required
`file`	`string`	The URL of an image or PDF file accessible by the server, or the Base64-encoded content of the above file types. For PDF files exceeding 10 pages, only the content of the first 10 pages will be used.	Yes
`fileType`	`integer` \| `null`	The file type. `0` indicates a PDF file, and `1` indicates an image file. If this attribute is missing in the request body, the file type will be inferred based on the URL.	No
`useDocOrientationClassify`	`boolean` \| `null`	Please refer to the description of the `use_doc_orientation_classify` parameter of the pipeline object's `predict` method.	No
`useDocUnwarping`	`boolean` \| `null`	Please refer to the description of the `use_doc_unwarping` parameter of the pipeline object's `predict` method.	No
`useLayoutDetection`	`boolean` \| `null`	Please refer to the description of the `use_layout_detection` parameter of the pipeline object's `predict` method.	No
`useOcrModel`	`boolean` \| `null`	Please refer to the description of the `use_ocr_model` parameter of the pipeline object's `predict` method.	No
`layoutThreshold`	`number` \| `null`	Please refer to the description of the `layout_threshold` parameter of the pipeline object's `predict` method.	No
`layoutNms`	`boolean` \| `null`	Please refer to the description of the `layout_nms` parameter of the pipeline object's `predict` method.	No
`layoutUnclipRatio`	`number` \| `array` \| `null`	Please refer to the description of the `layout_unclip_ratio` parameter of the pipeline object's `predict` method.	No
`layoutMergeBboxesMode`	`string` \| `null`	Please refer to the description of the `layout_merge_bboxes_mode` parameter of the pipeline object's `predict` method.	No
`textDetLimitSideLen`	`integer` \| `null`	Please refer to the description of the `text_det_limit_side_len` parameter of the pipeline object's `predict` method.	No
`textDetLimitType`	`string` \| `null`	Please refer to the description of the `text_det_limit_type` parameter of the pipeline object's `predict` method.	No
`textDetThresh`	`number` \| `null`	Please refer to the description of the `text_det_thresh` parameter of the pipeline object's `predict` method.	No
`textDetBoxThresh`	`number` \| `null`	Please refer to the description of the `text_det_box_thresh` parameter of the pipeline object's `predict` method.	No
`textDetUnclipRatio`	`number` \| `null`	Please refer to the description of the `text_det_unclip_ratio` parameter of the pipeline object's `predict` method.	No
`textRecScoreThresh`	`number` \| `null`	Please refer to the description of the `text_rec_score_thresh` parameter of the pipeline object's `predict` method.	No
`useTableCellsOcrResults`	`boolean`	Please refer to the description of the `use_table_cells_ocr_results` parameter of the pipeline object's `predict` method.	No

When the request is processed successfully, the result in the response body has the following properties:

Name	Type	Meaning
`tableRecResults`	`object`	The table recognition results. The array length is 1 (for image input) or the smaller of the number of document pages and 10 (for PDF input). For PDF input, each element in the array represents the processing result of each page in the PDF file.
`dataInfo`	`object`	Information about the input data.

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

Name	Type	Meaning
`prunedResult`	`object`	A simplified version of the `res` field in the JSON representation of the result generated by the `predict` method of the production object, with the `input_path` field removed.
`outputImages`	`object` \| `null`	Refer to the description of the `img` attribute of the production prediction results. The image is in JPEG format and encoded in Base64.
`inputImage`	`string` \| `null`	The input image. The image is in JPEG format and encoded in 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(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}")

📱 Edge Deployment: Edge deployment is a method of placing computing and data processing capabilities on the user's device itself, allowing the device to process data directly 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 for your model pipeline according to your needs, and then proceed with the subsequent AI application integration.

4. Custom Development¶

If the default model weights provided by the general table recognition pipeline are not satisfactory in terms of accuracy or speed in your scenario, you can try to further fine-tune the existing model using your own specific domain or application scenario data to improve the recognition performance of the general table recognition pipeline in your scenario.

4.1 Model Fine-tuning¶

Since the general table recognition pipeline consists of several modules, if the performance of the model pipeline does not meet expectations, the issue may originate from any one of these modules. You can analyze the images with poor recognition results to determine which module is problematic, and then refer to the corresponding fine-tuning tutorial links in the table below for model fine-tuning.

Scenario	Fine-tuning Module	Fine-tuning Reference Link
Table structure recognition error or cell positioning error	Table Structure Recognition Module	Link
Failed to detect the table area	Layout Area Detection Module	Link
Text detection omission	Text Detection Module	Link
Text content is inaccurate	Text Recognition Module	Link
Whole image rotation correction is inaccurate	Document Image Orientation Classification Module	Link
Image distortion correction is inaccurate	Text Image Correction Module	Fine-tuning not supported yet

4.2 Model Application¶

After you complete 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 production configuration file by replacing the local path of the fine-tuned model weights to the corresponding position in the production configuration file.

SubModules:
  LayoutDetection:
    module_name: layout_detection
    model_name: PicoDet_layout_1x_table
    model_dir: null # Replace with fine-tuned model weight paths

  TableStructureRecognition:
    module_name: table_structure_recognition
    model_name: SLANet_plus
    model_dir: null # Replace with fine-tuned model weight paths

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 # Replace with fine-tuned model weight paths

      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 # Replace with fine-tuned model weight paths
        limit_side_len: 960
        limit_type: max
        thresh: 0.3
        box_thresh: 0.4
        unclip_ratio: 2.0
      TextRecognition:
        module_name: text_recognition
        model_name: PP-OCRv4_server_rec
        model_dir: null # Replace with fine-tuned model weight paths
        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 production configuration file.

5. Multi-Hardware Support¶

PaddleX supports a variety of mainstream hardware devices such as NVIDIA GPU, Kunlunxin XPU, Ascend NPU, and Cambricon MLU. Simply modify the --device parameter to seamlessly switch between different hardware.

For example, if you use Ascend NPU for table recognition production inference, the CLI command is:

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

Of course, you can also specify the hardware device when calling create_pipeline() or predict() in the Python script.

If you want to use the universal table recognition pipeline on a wider range of hardware, please refer to the PaddleX Multi-Device Usage Guide.