Universal Layout Analysis v2 pipeline User Guide¶

1. Introduction to Universal Layout Analysis v2 pipeline¶

Layout analysis is a technology that extracts structured information from document images, primarily used to convert complex document layouts into machine-readable data formats. This technology is widely applied in document management, information extraction, and data digitization. By combining Optical Character Recognition (OCR), image processing, and machine learning algorithms, layout analysis can identify and extract text blocks, headings, paragraphs, images, tables, and other layout elements from documents. The process typically involves three main steps: layout analysis, element analysis, and data formatting, ultimately generating structured document data to improve the efficiency and accuracy of data processing. The Universal Layout Analysis v2 pipeline, based on the v1 pipeline, enhances the capabilities of layout region detection, table recognition, and formula recognition, and adds the ability to restore multi-column reading order and convert results into Markdown files. It performs excellently on various document data and can handle more complex document data. This pipeline also provides flexible service-oriented deployment options, supporting the use of multiple programming languages on various hardware. Moreover, this pipeline offers the capability for secondary development; you can train and optimize models on your own dataset based on this pipeline, and the trained models can be seamlessly integrated.

The Universal Layout Analysis v2 pipeline includes a mandatory layout region analysis module and a general OCR sub-pipeline, as well as optional sub-pipelines for document image preprocessing, table recognition, seal recognition, and formula recognition.

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

👉 Model List Details

Document Image Orientation Classification Module (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)	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 Image Rectification Module (Optional):

Model	Model Download Link	CER	Model Storage Size (M)	Introduction
UVDoc	Inference Model/Training Model	0.179	30.3 M	A high-precision text image rectification model.

Layout Detection Module Model (Required):

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)	Introduction
PP-DocLayout-L	Inference Model/Training Model	90.4	34.6244 / 10.3945	510.57 / -	123.76 M	A high-precision layout region detection model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, 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 detection model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, 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 detection model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, and research reports, based on PicoDet-S.
PicoDet_layout_1x	Inference Model/Training Model	86.8	9.03 / 3.10	25.82 / 20.70	7.4	A high-efficiency layout region 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 region detection model trained on a self-built 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 region detection model trained on a self-built dataset containing Chinese and English papers, magazines, and research reports, based on the lightweight PicoDet-S model, with 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 region detection model trained on a self-built dataset containing Chinese and English papers, magazines, and research reports, based on the lightweight PicoDet-S model, with 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, and 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 region detection model trained on a self-built dataset containing Chinese and English papers, magazines, and research reports, based on PicoDet-L, with 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 region detection model trained on a self-built dataset containing Chinese and English papers, magazines, and research reports, based on PicoDet-L, with 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, and 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 region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H, with 3 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 region localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H, with 17 common layout categories: paragraph title, image, text, number, abstract, content, chart title, formula, table, table title, references, document title, footnote, header, algorithm, footer, and seal.

Table Structure Recognition Module (Optional):

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)	Introduction
SLANet	Inference Model/Training Model	59.52	103.08 / 103.08	197.99 / 197.99	6.9 M	SLANet is a table structure recognition model independently developed by the Baidu PaddlePaddle Vision Team. This model significantly improves the accuracy and inference speed of table structure recognition by using a lightweight backbone network PP-LCNet that is friendly to CPUs, 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	SLANet_plus is the enhanced version of the SLANet table structure recognition model independently developed by the Baidu PaddlePaddle Vision Team. Compared to SLANet, SLANet_plus has significantly improved the ability to recognize wireless and complex tables and reduced the model's sensitivity to table positioning accuracy. Even if there is a deviation in table positioning, it can still recognize accurately.

Text Detection Module (Required):

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	82.56	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	77.35	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.
PP-OCRv3_mobile_det	Inference Model/Training Model	78.68	8.44 / 2.91	27.87 / 27.87	2.1	The mobile text detection model of PP-OCRv3, with higher efficiency, suitable for deployment on edge devices.
PP-OCRv3_server_det	Inference Model/Training Model	80.11	65.41 / 13.67	305.07 / 305.07	102.1	The server-side text detection model of PP-OCRv3, with higher accuracy, suitable for deployment on high-performance servers.

Text Recognition Module Model (Required):

* 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.

Text Line Orientation Classification Module (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)	Introduction
PP-LCNet_x0_25_textline_ori	Inference Model/Training Model	95.54	-	-	0.32	A text line classification model based on PP-LCNet_x0_25, with two categories: 0 degrees and 180 degrees.

Formula Recognition Module (Optional):

Model	Model Download Link	BLEU Score	Normed Edit Distance	ExpRate (%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size
LaTeX_OCR_rec	Inference Model/Training Model	0.8821	0.0823	40.01	2047.13 / 2047.13	10582.73 / 10582.73	89.7 M

Seal Text Detection Module (Optional):

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_seal_det	Inference Model/Training Model	98.21	74.75 / 67.72	382.55 / 382.55	109	The PP-OCRv4 server seal text detection model offers higher precision and is suitable for deployment on high-performance servers.
PP-OCRv4_mobile_seal_det	Inference Model/Training Model	96.47	7.82 / 3.09	48.28 / 23.97	4.6	The PP-OCRv4 mobile seal text detection model provides higher efficiency and is suitable for deployment on edge devices.

Text Image Rectification Module Model:

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

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

Document Image Orientation Classification Module Model:

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)	Introduction
PP-LCNet_x1_0_doc_ori	Inference Model/Training Model	99.06	2.31 / 0.43	3.37 / 1.27	7	The document image classification model based on PP-LCNet_x1_0 includes four categories: 0 degrees, 90 degrees, 180 degrees, and 270 degrees.

**Test Environment Description**: - **Performance Test Environment** - **Test Dataset**: - Text Image Rectification Model: [DocUNet](https://www3.cs.stonybrook.edu/~cvl/docunet.html). - Layout Region Detection Model: A self-built layout analysis dataset using PaddleOCR, containing 10,000 images of common document types such as Chinese and English papers, magazines, and research reports. - Table Structure Recognition Model: A self-built English table recognition dataset using PaddleX. - Text Detection Model: A self-built Chinese dataset using PaddleOCR, covering multiple 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 multiple scenarios such as street scenes, web images, documents, and handwriting, with 11,000 images for text recognition. - ch_SVTRv2_rec: Evaluation set A for "OCR End-to-End Recognition Task" in the [PaddleOCR Algorithm Model Challenge](https://aistudio.baidu.com/competition/detail/1131/0/introduction). - ch_RepSVTR_rec: Evaluation set B for "OCR End-to-End Recognition Task" in the [PaddleOCR Algorithm Model Challenge](https://aistudio.baidu.com/competition/detail/1131/0/introduction). - English Recognition Model: A self-built English dataset using PaddleX. - Multilingual Recognition Model: A self-built multilingual dataset using PaddleX. - Text Line Orientation Classification Model: A self-built dataset using PaddleX, covering various scenarios such as ID cards and documents, containing 1000 images. - Seal Text Detection Model: A self-built dataset using PaddleX, containing 500 images of circular seal textures. - **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 the model pipelines provided by PaddleX can be quickly experienced. You can use the command line or Python on your local machine to experience the effect of the General Layout Parsing v2 pipeline.

Before using the General Layout Parsing v2 pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX Local Installation Guide.

2.1 Experiencing via Command Line¶

You can quickly experience the layout parsing pipeline with a single command. Use the test file and replace --input with the local path to perform prediction.

paddlex --pipeline layout_parsing_v2 \
        --input layout_parsing_v2_demo.png \
        --use_doc_orientation_classify False \
        --use_doc_unwarping False \
        --use_textline_orientation False \
        --save_path ./output \
        --device gpu:0

The parameter description can be found in 2.2.2 Python Script Integration.

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

👉Click to Expand



{'res': {'input_path': 'layout_parsing_v2_demo.png', 'model_settings': {'use_doc_preprocessor': False, 'use_general_ocr': True, 'use_seal_recognition': True, 'use_table_recognition': True, 'use_formula_recognition': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 2, 'label': 'text', 'score': 0.9853514432907104, 'coordinate': [770.9531, 776.6814, 1122.6057, 1058.7322]}, {'cls_id': 1, 'label': 'image', 'score': 0.9848673939704895, 'coordinate': [775.7434, 202.27979, 1502.8113, 686.02136]}, {'cls_id': 2, 'label': 'text', 'score': 0.983731746673584, 'coordinate': [1152.3197, 1113.3275, 1503.3029, 1346.586]}, {'cls_id': 2, 'label': 'text', 'score': 0.9832221865653992, 'coordinate': [1152.5602, 801.431, 1503.8436, 986.3563]}, {'cls_id': 2, 'label': 'text', 'score': 0.9829439520835876, 'coordinate': [9.549545, 849.5713, 359.1173, 1058.7488]}, {'cls_id': 2, 'label': 'text', 'score': 0.9811657667160034, 'coordinate': [389.58298, 1137.2659, 740.66235, 1346.7488]}, {'cls_id': 2, 'label': 'text', 'score': 0.9775941371917725, 'coordinate': [9.1302185, 201.85, 359.0409, 339.05692]}, {'cls_id': 2, 'label': 'text', 'score': 0.9750366806983948, 'coordinate': [389.71454, 752.96924, 740.544, 889.92456]}, {'cls_id': 2, 'label': 'text', 'score': 0.9738152027130127, 'coordinate': [389.94565, 298.55988, 740.5585, 435.5124]}, {'cls_id': 2, 'label': 'text', 'score': 0.9737328290939331, 'coordinate': [771.50256, 1065.4697, 1122.2582, 1178.7324]}, {'cls_id': 2, 'label': 'text', 'score': 0.9728517532348633, 'coordinate': [1152.5154, 993.3312, 1503.2349, 1106.327]}, {'cls_id': 2, 'label': 'text', 'score': 0.9725610017776489, 'coordinate': [9.372787, 1185.823, 359.31738, 1298.7227]}, {'cls_id': 2, 'label': 'text', 'score': 0.9724331498146057, 'coordinate': [389.62848, 610.7389, 740.83234, 746.2377]}, {'cls_id': 2, 'label': 'text', 'score': 0.9720287322998047, 'coordinate': [389.29898, 897.0936, 741.41516, 1034.6616]}, {'cls_id': 2, 'label': 'text', 'score': 0.9713053703308105, 'coordinate': [10.323685, 1065.4663, 359.6786, 1178.8872]}, {'cls_id': 2, 'label': 'text', 'score': 0.9689728021621704, 'coordinate': [9.336395, 537.6609, 359.2901, 652.1881]}, {'cls_id': 2, 'label': 'text', 'score': 0.9684857130050659, 'coordinate': [10.7608185, 345.95068, 358.93616, 434.64087]}, {'cls_id': 2, 'label': 'text', 'score': 0.9681928753852844, 'coordinate': [9.674866, 658.89075, 359.56528, 770.4319]}, {'cls_id': 2, 'label': 'text', 'score': 0.9634978175163269, 'coordinate': [770.9464, 1281.1785, 1122.6522, 1346.7156]}, {'cls_id': 2, 'label': 'text', 'score': 0.96304851770401, 'coordinate': [390.0113, 201.28055, 740.1684, 291.53073]}, {'cls_id': 2, 'label': 'text', 'score': 0.962053120136261, 'coordinate': [391.21393, 1040.952, 740.5046, 1130.32]}, {'cls_id': 2, 'label': 'text', 'score': 0.9565253853797913, 'coordinate': [10.113251, 777.1482, 359.439, 842.437]}, {'cls_id': 2, 'label': 'text', 'score': 0.9497362375259399, 'coordinate': [390.31357, 537.86285, 740.47595, 603.9285]}, {'cls_id': 2, 'label': 'text', 'score': 0.9371236562728882, 'coordinate': [10.2034, 1305.9753, 359.5958, 1346.7295]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9338151216506958, 'coordinate': [791.6062, 1200.8479, 1103.3257, 1259.9324]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9326773285865784, 'coordinate': [408.0737, 457.37024, 718.9509, 516.63464]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9274250864982605, 'coordinate': [29.448685, 456.6762, 340.99194, 515.6999]}, {'cls_id': 2, 'label': 'text', 'score': 0.8742568492889404, 'coordinate': [1154.7095, 777.3624, 1330.3086, 794.5853]}, {'cls_id': 2, 'label': 'text', 'score': 0.8442489504814148, 'coordinate': [586.49316, 160.15454, 927.468, 179.64203]}, {'cls_id': 11, 'label': 'doc_title', 'score': 0.8332607746124268, 'coordinate': [133.80017, 37.41908, 1380.8601, 124.1429]}, {'cls_id': 6, 'label': 'figure_title', 'score': 0.6770150661468506, 'coordinate': [812.1718, 705.1199, 1484.6973, 747.1692]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[ 133,   35],
        ...,
        [ 133,  131]],

       ...,

       [[1154, 1323],
        ...,
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       ...,
       [1152, ..., 1359]], dtype=int16)}}}

The result parameter description can be found in the result interpretation in 2.2.2 Python Script Integration.

Note: Since the default model of the pipeline is relatively large, the inference speed may be slow. You can refer to the model list in Section 1 and replace it with a model that has faster inference speed.

2.2 Python Script Integration¶

Just a few lines of code can complete the quick inference of the pipeline. Taking the General Layout Analysis v2 pipeline as an example:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="layout_parsing_v2")

output = pipeline.predict(
    input="./layout_parsing_v2_demo.png",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=False,
)
for res in output:
    res.print() ## Print the structured prediction output
    res.save_to_json(save_path="output") ## Save the structured JSON result of the current image
    res.save_to_markdown(save_path="output") ## Save the result of the current image in Markdown format

If it is a PDF file, each page of the PDF will be processed separately, and each page will have its own corresponding Markdown file. If you want to convert the entire PDF file into a Markdown file, it is recommended to run it in the following way:

from pathlib import Path
from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="layout_parsing_v2")

input_file = "./your_pdf_file.pdf"
output_path = Path("./output")

output = pipeline.predict(
    input=input_file,
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=False)

markdown_texts = ""
markdown_images = []

for res in output:
    markdown_texts += res.markdown["markdown_texts"]
    markdown_images.append(res.markdown["markdown_images"])

mkd_file_path = output_path / f"{Path(input_file).stem}.md"
mkd_file_path.parent.mkdir(parents=True, exist_ok=True)
with open(mkd_file_path, "w", encoding="utf-8") as f:
    f.write(markdown_texts)

for item in markdown_images:
    if item:
        for path, image in item.items():
            file_path = output_path / path
            file_path.parent.mkdir(parents=True, exist_ok=True)
            image.save(file_path)

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

(1) Instantiate the create_pipeline instance to create a pipeline object. The specific parameter descriptions are 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 a pipeline supported by PaddleX.	`str`	`None`
`config`	The path to the pipeline configuration file.	`str`	`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", or CPU, such 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 pipeline object to perform inference prediction. This method will return a generator. Below are the parameters and their descriptions for the predict() method:

Parameter	Parameter Description	Parameter 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 an image file or PDF file, such as `/root/data/img.jpg`; URL link, such as the web URL of an image file or PDF file: example; Local directory, the directory should contain images to be predicted, such as the local path: `/root/data/` (currently does not support prediction of PDF files in directories; PDF files must be specified with an exact file path) List: Elements of the list 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`	Production inference device	`str\|None`	CPU: `cpu` indicates using CPU for inference; GPU: `gpu:0` indicates using the first GPU for inference; NPU: `npu:0` indicates using the first NPU for inference; XPU: `xpu:0` indicates using the first XPU for inference; MLU: `mlu:0` indicates using the first MLU for inference; DCU: `dcu:0` indicates using the first DCU for inference; None: If set to `None`, the default value initialized in the pipeline will be used. During initialization, the local GPU device 0 will be prioritized; if unavailable, the CPU device 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 in the pipeline 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 in the pipeline will be used, initialized as `True`;	`None`
`use_textline_orientation`	Whether to use the text line orientation classification module	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized in the pipeline will be used, initialized as `True`;	`None`
`use_general_ocr`	Whether to use the OCR sub-line	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized in the pipeline will be used, initialized as `True`;	`None`
`use_seal_recognition`	Whether to use the seal recognition sub-line	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized in the pipeline will be used, initialized as `True`;	`None`
`use_table_recognition`	Whether to use the table recognition sub-line	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `True`;	`None`
`use_formula_recognition`	Whether to use the formula recognition sub-line	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `True`;	`None`
`layout_threshold`	Layout model score threshold	`float\|dict\|None`	float: Any floating-point number between `0` and `1`; dict: `{0:0.1}` where the key is the category ID and the value is the threshold for that category; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `0.5`;	`None`
`layout_nms`	Whether the layout area detection model uses NMS post-processing	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `True`;	`None`
`layout_unclip_ratio`	Expansion ratio of the detection box for the layout area detection model	`float\|Tuple[float,float]\|None`	float: Any floating-point number greater than `0`; Tuple[float,float]: The expansion ratios in the horizontal and vertical directions, respectively; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `1.0`;	`None`
`layout_merge_bboxes_mode`	Overlap box filtering method for layout area detection	`str\|None`	str: `large`, `small`, `union`, representing whether to retain the larger box, the smaller box, or both during overlap box filtering; None: If set to `None`, the default value initialized for this parameter in the pipeline will be used, initialized as `large`;	`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 for this parameter in the pipeline 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 that the shortest side of the image is not less than `det_limit_side_len`, and `max` ensures that the longest side of the image is not greater than `limit_side_len`. None: If set to `None`, the default value initialized in the pipeline will be used, initialized as `max`.	`None`
`text_det_thresh`	Detection pixel threshold. In the output probability map, only 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 pipeline will be used, which is `0.3`.	`None`
`text_det_box_thresh`	Detection box threshold. The detection result will be considered as a text area only if the average score of all pixels within the bounding box is greater than this threshold.	`float\|None`	float: Any floating-point number greater than `0`. None: If set to `None`, the default value initialized in the pipeline will be used, which is `0.6`.	`None`
`text_det_unclip_ratio`	Text detection expansion ratio. This method is used to expand the text area. The larger this 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 pipeline will be used, which is `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 the production-initialized value of this parameter `0.0`. That is, no threshold is set	`None`
`seal_det_limit_side_len`	Image side length limit for seal detection	`int\|None`	int: Any integer greater than `0` None: If set to `None`, it will default to the production-initialized value of this parameter, initialized as `960`	`None`
`seal_det_limit_type`	Type of image side length limit for seal detection	`str\|None`	str: Supports `min` and `max`, `min` indicates that the shortest side of the image is not less than `det_limit_side_len`, `max` indicates that the longest side of the image is not greater than `limit_side_len` None: If set to `None`, it will default to the production-initialized value of this parameter, initialized as `max`	`None`
`seal_det_thresh`	Detection pixel threshold, in the output probability map, pixels with scores greater than this threshold will be considered as seal pixels	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to the production-initialized value of this parameter `0.3`	`None`
`seal_det_box_thresh`	Detection box threshold, within the detection result bounding box, if the average score of all pixels is greater than this threshold, the result will be considered as a seal area	`float\|None`	float: Any floating-point number greater than `0` None: If set to `None`, it will default to the production-initialized value of this parameter `0.6`	`None`
`seal_det_unclip_ratio`	Expansion ratio for seal detection, this method is used to expand the text area, the larger this 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 the production-initialized value of this parameter `2.0`	`None`
`seal_rec_score_thresh`	Seal 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 the production-initialized value of this parameter `0.0`. That is, no threshold is set	`None`

(3) Process the prediction results: The prediction result of each sample is a corresponding Result object, and it supports operations such as printing, saving as an image, 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. 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` will retain 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 file path for saving. When it is a directory, the saved file will have the same name as the input file	None
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data, making 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` will retain the original characters. Only effective when `format_json` is `True`	`False`
`save_to_img()`	Save the visualization images of each module in PNG format	`save_path`	`str`	The file path for saving, supporting both directory and file paths	None
`save_to_markdown()`	Saves each page of the image or PDF file as a markdown formatted file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	None
`save_to_html()`	Save the table in the file as an HTML file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	None
`save_to_xlsx()`	Save the table in the file as an XLSX file	`save_path`	`str`	The file path for saving, supporting both directory and file paths	None

Calling the print() method will print the results to the terminal. 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, this indicates which page of the PDF it is; otherwise, it is None.
- model_settings: (Dict[str, bool]) Model parameters required for configuring the pipeline.
  - use_doc_preprocessor: (bool) Controls whether to enable the document preprocessor sub-line.
  - use_general_ocr: (bool) Controls whether to enable the OCR sub-line.
  - use_seal_recognition: (bool) Controls whether to enable the seal recognition sub-line.
  - use_table_recognition: (bool) Controls whether to enable the table recognition sub-line.
  - use_formula_recognition: (bool) Controls whether to enable the formula recognition sub-line.
- parsing_res_list: (List[Dict]) A list of parsing results, where each element is a dictionary. The order of the list is the reading order after parsing.
  - layout_bbox: (np.ndarray) The bounding box of the layout area.
  - {label}: (str) The key is the label of the layout area, such as text, table, etc., and the content is the content within the layout area.
  - layout: (str) The layout type, such as double, single, etc.
- overall_ocr_res: (Dict[str, Union[List[str], List[float], numpy.ndarray]]) A dictionary of global OCR results
- input_path: (Union[str, None]) The image path accepted by the image OCR sub-line. When the input is a numpy.ndarray, it is saved as None.
- model_settings: (Dict) Model configuration parameters for the OCR sub-line.
- dt_polys: (List[numpy.ndarray]) A list of polygon boxes for text detection. Each detection box is represented by a numpy array of 4 vertex coordinates, with 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 side length limits.
  - thresh: (float) The confidence threshold for text pixel classification.
  - box_thresh: (float) The confidence threshold for text detection boxes.
  - unclip_ratio: (float) The expansion ratio for text detection boxes.
  - text_type: (str) The type of text detection, currently fixed as "general".
- text_type: (str) The type of text detection, currently fixed as "general".
- textline_orientation_angles: (List[int]) The prediction results for text line orientation classification. When enabled, it returns the actual angle values (e.g., [0,0,1]).
- text_rec_score_thresh: (float) The filtering threshold for text recognition results.
- rec_texts: (List[str]) A list of text recognition results, containing only texts with confidence scores above text_rec_score_thresh.
- rec_scores: (List[float]) A list of confidence scores for text recognition, filtered by text_rec_score_thresh.
- rec_polys: (List[numpy.ndarray]) A list of text detection boxes filtered by confidence score, in the same format as dt_polys.
- text_paragraphs_ocr_res: (Dict[str, Union[List[str], List[float], numpy.ndarray]]) OCR results for paragraphs, excluding paragraphs of layout types such as tables, seals, and formulas.
  - rec_polys: (List[numpy.ndarray]) A list of text detection boxes, in the same format as dt_polys.
  - rec_texts: (List[str]) A list of text recognition results.
  - rec_scores: (List[float]) A list of confidence scores for text recognition results.
  - 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 a rectangle.
- formula_res_list: (List[Dict[str, Union[numpy.ndarray, List[float], str]]]) A list of formula recognition results, where each element is a dictionary.
  - rec_formula: (str) The result of formula recognition.
  - rec_polys: (numpy.ndarray) The detection box for the formula, with a shape of (4, 2) and a dtype of int16.
  - formula_region_id: (int) The region ID where the formula is located.
- seal_res_list: (List[Dict[str, Union[numpy.ndarray, List[float], str]]]) A list of seal recognition results, where each element is a dictionary.
  - input_path: (str) The input path of the seal image.
  - model_settings: (Dict) Model configuration parameters for the seal recognition sub-line.
  - dt_polys: (List[numpy.ndarray]) A list of seal detection boxes, in the same format as dt_polys.
  - text_det_params: (Dict[str, Dict[str, int, float]]) Configuration parameters for the seal detection module, with the same specific parameter meanings as above.
  - text_type: (str) The type of seal detection, currently fixed as "seal".
  - text_rec_score_thresh: (float) The filtering threshold for seal recognition results.
  - rec_texts: (List[str]) A list of seal recognition results, containing only texts with confidence scores above text_rec_score_thresh.
  - rec_scores: (List[float]) A list of confidence scores for seal recognition, filtered by text_rec_score_thresh.
  - rec_polys: (List[numpy.ndarray]) A list of seal detection boxes filtered by confidence score, in the same format as dt_polys.
  - rec_boxes: (numpy.ndarray) An array of rectangular bounding boxes for detection boxes, with a shape of (n, 4) and a dtype of int16. Each row represents a rectangle.
- table_res_list: (List[Dict[str, Union[numpy.ndarray, List[float], str]]]) A list of table recognition results, where each element is a dictionary.
  - cell_box_list: (List[numpy.ndarray]) A list of bounding boxes for table cells.
  - pred_html: (str) The HTML format string of the table.
  - table_ocr_pred: (dict) The OCR recognition results of the table.
    - rec_polys: (List[numpy.ndarray]) A list of detection boxes for cells.
    - rec_texts: (List[str]) The recognition results of cells.
    - rec_scores: (List[float]) The recognition confidence scores of cells.
    - 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 a rectangle.
Calling the save_to_json() method will save the above content to the specified save_path. If a directory is specified, the save path will be save_path/{your_img_basename}_res.json. If a file is specified, it will be saved directly to that file. Since JSON files do not support saving NumPy arrays, 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 a directory is specified, it will save images such as layout detection visualization, global OCR visualization, and layout reading order visualization. If a file is specified, 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 image will be retained.)
Calling the save_to_markdown() method will save the converted Markdown file to the specified save_path. The save path will be save_path/{your_img_basename}.md. If the input is a PDF file, it is recommended to specify a directory directly; otherwise, multiple Markdown files will be overwritten.

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
`img`	Get the visualized image in `dict` format	Property	Description
`json`	Get the prediction result in `json` format
`img`	Get the visualization image in `dict` format
`img`	Get the visualization image in `dict` format
`markdown`	Get the markdown result 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 layout_det_res, overall_ocr_res, text_paragraphs_ocr_res, formula_res_region1, table_cell_img, and seal_res_region1, and the corresponding values are Image.Image objects: used to display the visualized images of layout detection, OCR, OCR text paragraphs, formulas, tables, and seal results. If the optional module is not used, the dictionary only contains layout_det_res.
- The markdown property returns the prediction result as a dictionary. The keys are markdown_texts and markdown_images, corresponding to markdown text and images for display in Markdown (Image.Image objects).

In addition, you can obtain the layout parsing 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 layout_parsing_v2 --save_path ./my_path

If you have obtained the configuration file, you can customize the layout parsing pipeline configuration. You just need to 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/layout_parsing_v2.yaml")
output = pipeline.predict(
    input="./layout_parsing_v2_demo.png",,
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=False,
)
for res in output:
    res.print()
    res.print() ## Print the structured prediction output
    res.save_to_json(save_path="output") ## Save the structured JSON result of the current image
    res.save_to_markdown(save_path="output") ## Save the result of the current image in Markdown format

Note: The parameters in the configuration file are the pipeline initialization parameters. If you wish to change the initialization parameters of the General Layout Parsing 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 a configuration file, simply 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 proceed directly with development integration/deployment.

If you need to integrate the pipeline directly into 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 aimed at deeply optimizing 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.

☁️ Service-Oriented Deployment: Service-oriented deployment is a common form of deployment in actual production environments. By encapsulating the inference functionality into a service, clients can access these services through network requests to obtain inference results. PaddleX supports various service-oriented deployment solutions for pipelines. For detailed procedures, please refer to the PaddleX Service-Oriented Deployment Guide.

Below is the API reference for basic service-oriented deployment and examples of service calls in multiple languages:

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

Perform layout parsing.

POST /layout-parsing

The attributes of the request body are as follows:

Name	Type	Meaning	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 with more than 10 pages, only the content of the first 10 pages will be used.	Yes
`fileType`	`integer`｜`null`	File type. `0` represents a PDF file, and `1` represents an image file. If this attribute is missing from the request body, the file type will be inferred based on the URL.	No
`useDocOrientationClassify`	`boolean` \| `null`	Refer to the `use_doc_orientation_classify` parameter description in the pipeline `predict` method.	No
`useDocUnwarping`	`boolean` \| `null`	Refer to the `use_doc_unwarping` parameter description in the pipeline `predict` method.	No
`useTextlineOrientation`	`boolean` \| `null`	Refer to the `use_textline_orientation` parameter description in the pipeline `predict` method.	No
`useGeneralOcr`	`boolean` \| `null`	Refer to the `use_general_ocr` parameter description in the pipeline `predict` method.	No
`useSealRecognition`	`boolean` \| `null`	Refer to the `use_seal_recognition` parameter description in the pipeline `predict` method.	No
`useTableRecognition`	`boolean` \| `null`	Refer to the `use_table_recognition` parameter description in the pipeline `predict` method.	No
`useFormulaRecognition`	`boolean` \| `null`	Refer to the parameter description of `use_formula_recognition` in the `predict` method of the pipeline.	No
`textDetLimitSideLen`	`integer` \| `null`	Refer to the parameter description of `text_det_limit_side_len` in the `predict` method of the pipeline.	No
`textDetLimitType`	`string` \| `null`	Refer to the parameter description of `text_det_limit_type` in the `predict` method of the pipeline.	No
`textDetThresh`	`number` \| `null`	Refer to the parameter description of `text_det_thresh` in the `predict` method of the pipeline.	No
`textDetBoxThresh`	`number` \| `null`	Refer to the parameter description of `text_det_box_thresh` in the `predict` method of the pipeline.	No
`textDetUnclipRatio`	`number` \| `null`	Refer to the parameter description of `text_det_unclip_ratio` in the `predict` method of the pipeline.	No
`textRecScoreThresh`	`number` \| `null`	Refer to the parameter description of `text_rec_score_thresh` in the `predict` method of the pipeline.	No
`sealDetLimitSideLen`	`integer` \| `null`	Refer to the parameter description of `seal_det_limit_side_len` in the `predict` method of the pipeline.	No
`sealDetLimitType`	`string` \| `null`	Refer to the parameter description of `seal_det_limit_type` in the `predict` method of the pipeline.	No
`sealDetThresh`	`number` \| `null`	Refer to the parameter description of `seal_det_thresh` in the `predict` method of the pipeline.	No
`sealDetBoxThresh`	`number` \| `null`	Refer to the parameter description of `seal_det_box_thresh` in the `predict` method of the pipeline.	No
`sealDetUnclipRatio`	`number` \| `null`	Refer to the parameter description of `seal_det_unclip_ratio` in the `predict` method of the pipeline.	No
`sealRecScoreThresh`	`number` \| `null`	Refer to the parameter description of `seal_rec_score_thresh` in the `predict` method of the pipeline.	No
`layoutThreshold`	`number` \| `null`	Refer to the parameter description of `layout_threshold` in the `predict` method of the pipeline.	No
`layoutNms`	`boolean` \| `null`	Refer to the parameter description of `layout_nms` in the `predict` method of the pipeline.	No
`layoutUnclipRatio`	`number` \| `array` \| `null`	Refer to the parameter description of `layout_unclip_ratio` in the `predict` method of the pipeline.	No
`layoutMergeBboxesMode`	`string` \| `null`	Refer to the parameter description of `layout_merge_bboxes_mode` in the `predict` method of the pipeline.	No

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

Name	Type	Meaning
`layoutParsingResults`	`array`	The layout parsing results. The length of the array is 1 (for image input) or the minimum of the document page count 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 layoutParsingResults is an object with the following attributes:

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 pipeline object, with the `input_path` field removed.
`markdown`	`object`	The Markdown result.
`outputImages`	`object` \| `null`	See the description of the `img` attribute in the result of the pipeline prediction. The images are in JPEG format and are Base64-encoded.
`inputImage`	`string` \| `null`	The input image. The image is in JPEG format and is Base64-encoded.

markdown is an object with the following attributes:

Name	Type	Meaning
`text`	`string`	The Markdown text.
`images`	`object`	A key-value pair of relative paths of Markdown images and Base64-encoded images.
`isStart`	`boolean`	Whether the first element on the current page is the start of a segment.
`isEnd`	`boolean`	Whether the last element on the current page is the end of a segment.

Multi-language Service Call Example

Python

import base64
import requests

API_URL = "http://localhost:8080/layout-parsing" # Service URL

image_path = "./demo.jpg"

# Encode the local image with Base64
with open(image_path, "rb") as file:
    image_bytes = file.read()
    image_data = base64.b64encode(image_bytes).decode("ascii")

payload = {
    "file": image_data, # Base64-encoded file content or file URL
}

# Call the API
response = requests.post(API_URL, json=payload)

# Process the response data
assert response.status_code == 200
result = response.json()["result"]
print("\nDetected layout elements:")
for res in result["layoutParsingResults"]:
    print(res["prunedResult"])
    md_dir = pathlib.Path(f"markdown_{i}")
    md_dir.mkdir(exist_ok=True)
    (md_dir / "doc.md").write_text(res["markdown"]["text"])
    for img_path, img in res["markdown"]["images"].items():
        img_path = md_dir / img_path
        img_path.parent.mkdir(parents=True, exist_ok=True)
        img_path.write_bytes(base64.b64decode(img))
    print(f"Markdown document saved at {md_dir / 'doc.md'}")
    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 directly on user devices, allowing the device 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 into your pipeline and proceed with subsequent AI application integration.

4. Secondary Development¶

If the default model weights provided by the General Layout Parsing v2 pipeline do not meet your requirements in terms of accuracy or speed, you can try to fine-tune the existing model using your own domain-specific or application-specific data to improve the recognition performance of the General Layout Parsing v2 pipeline in your scenario.

4.1 Model Fine-Tuning¶

Since the General Layout Parsing v2 pipeline consists of 7 modules, the unsatisfactory performance of the pipeline may originate from any one of these modules.

Since the General Layout Parsing v2 pipeline includes several modules, the unsatisfactory performance of the pipeline may originate from any one of these modules. You can analyze the cases with poor extraction results, identify which module is problematic through visualizing the images, and refer to the corresponding fine-tuning tutorial links in the table below to fine-tune the model.

Scenario	Fine-tuning Module	Fine-tuning Reference Link
Inaccurate layout region detection, such as missed detection of seals, tables, etc.	Layout Region Detection Module	Link
Inaccurate table structure recognition	Table Structure Recognition Module	Link
Inaccurate formula recognition	Formula Recognition Module	Link
Missed detection of seal text	Seal Text Detection Module	Link
Missed detection of text	Text Detection Module	Link
Inaccurate text content	Text Recognition Module	Link
Inaccurate correction of vertical or rotated text lines	Text Line Orientation Classification Module	Link
Inaccurate correction of whole-image rotation	Document Image Orientation Classification Module	Link
Inaccurate correction of image distortion	Text Image Correction Module	Fine-tuning not supported

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: PP-DocLayout-L
    model_dir: null
......
SubPipelines:
  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_rec_doc
        model_dir: null
        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
        batch_size: 1
        score_thresh: 0
......

Subsequently, refer to the command-line method or Python script method in the local experience to load the modified pipeline 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 devices.

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

paddlex --pipeline layout_parsing_v2 \
        --input layout_parsing_v2_demo.png  \
        --use_doc_orientation_classify False \
        --use_doc_unwarping False \
        --use_textline_orientation False \
        --save_path ./output \
        --device npu:0

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

If you want to use the Universal Layout Parsing v2 pipeline on a wider range of hardware, please refer to the PaddleX Multi-Device Usage Guide.