Seal Text Recognition Pipeline Tutorial¶

1. Introduction to Seal Text Recognition Pipeline¶

Seal text recognition is a technology that automatically extracts and recognizes the content of seals from documents or images. The recognition of seal text is part of document processing and has many applications in various scenarios, such as contract comparison, warehouse entry and exit review, and invoice reimbursement review.

The seal text recognition pipeline is used to recognize the text content of seals, extracting the text information from seal images and outputting it in text form. This pipeline integrates the industry-renowned end-to-end OCR system PP-OCRv4, supporting the detection and recognition of curved seal text. Additionally, this pipeline integrates an optional layout region localization module, which can accurately locate the layout position of the seal within the entire document. It also includes optional document image orientation correction and distortion correction functions. Based on this pipeline, millisecond-level accurate text content prediction can be achieved on a CPU. This pipeline also provides flexible service deployment methods, supporting the use of multiple programming languages on various hardware. Moreover, it offers secondary development capabilities, allowing you to train and fine-tune on your own dataset based on this pipeline, and the trained model can be seamlessly integrated.

The seal text recognition pipeline includes a seal text detection module and a text recognition module, as well as optional layout detection module, document image orientation classification module, and text image correction module.

If you prioritize model accuracy, choose a model with higher accuracy. If you prioritize inference speed, choose a model with faster inference speed. If you prioritize model storage size, choose a model with smaller storage size.

Layout Region Detection Module (Optional):

Layout detection model, including 23 common categories: document title, paragraph title, text, page number, abstract, table of contents, references, footnotes, header, footer, algorithm, formula, formula number, image, chart title, table, table title, seal, chart title, chart, header image, footer image, sidebar text

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 area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.
PP-DocLayout-M	Inference Model/Training Model	75.2	13.3259 / 4.8685	44.0680 / 44.0680	22.578	A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using 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 area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.

❗ The above list includes the 3 core models that are key supported by the text recognition module. The module actually supports a total of 11 full models, including several predefined models with different categories. The complete model list is as follows:

👉 Details of Model List

* Table Layout Detection Model

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_table	Inference Model/Training Model	97.5	8.02 / 3.09	23.70 / 20.41	7.4 M	A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting table regions.

Note: The evaluation dataset for the above precision metrics is a self-built layout table area detection dataset by PaddleOCR, containing 7835 Chinese and English document images with tables. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision. * 3-Class Layout Detection Model, including Table, Image, and Stamp

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-S_layout_3cls	Inference Model/Training Model	88.2	8.99 / 2.22	16.11 / 8.73	4.8	A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.
PicoDet-L_layout_3cls	Inference Model/Training Model	89.0	13.05 / 4.50	41.30 / 41.30	22.6	A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.
RT-DETR-H_layout_3cls	Inference Model/Training Model	95.8	114.93 / 27.71	947.56 / 947.56	470.1	A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.

* 5-Class English Document Area Detection Model, including Text, Title, Table, Image, and List

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	97.8	9.03 / 3.10	25.82 / 20.70	7.4	A high-efficiency English document layout area localization model trained on the PubLayNet dataset using PicoDet-1x.

Note: The evaluation dataset for the above precision metrics is the [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) dataset, containing 11245 English document images. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision. * 17-Class Area Detection Model, including 17 common layout categories: Paragraph Title, Image, Text, Number, Abstract, Content, Figure Caption, Formula, Table, Table Caption, References, Document Title, Footnote, Header, Algorithm, Footer, and Stamp

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-S_layout_17cls	Inference Model/Training Model	87.4	9.11 / 2.12	15.42 / 9.12	4.8	A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.
PicoDet-L_layout_17cls	Inference Model/Training Model	89.0	13.50 / 4.69	43.32 / 43.32	22.6	A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.
RT-DETR-H_layout_17cls	Inference Model/Training Model	98.3	115.29 / 104.09	995.27 / 995.27	470.2	A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.

Document Image Orientation Classification Module (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)	Description
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 Correction Module (Optional):

Model	Model Download Link	CER	Model Storage Size (M)	Description
UVDoc	Inference Model/Training Model	0.179	30.3 M	High-precision text image correction model

Text Detection Module:

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)	Description
PP-OCRv4_server_seal_det	Inference Model/Trained Model	98.21	74.75 / 67.72	382.55 / 382.55	109	PP-OCRv4 server-side seal text detection model, with higher accuracy, suitable for deployment on better servers
PP-OCRv4_mobile_seal_det	Inference Model/Trained Model	96.47	7.82 / 3.09	48.28 / 23.97	4.6	PP-OCRv4 mobile-side seal text detection model, with higher efficiency, suitable for deployment on the edge

Text Recognition Module:

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_server_rec_doc	Inference Model/Training Model	81.53	6.65 / 2.38	32.92 / 32.92	74.7 M	PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the ability to recognize some traditional Chinese characters, Japanese, and special characters, and can support the recognition of more than 15,000 characters. In addition to improving the text recognition capability related to documents, it also enhances the general text recognition capability.
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.74	4.82 / 1.20	16.74 / 4.64	10.6 M	The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.
PP-OCRv4_server_rec	Inference Model/Training Model	80.61	6.58 / 2.43	33.17 / 33.17	71.2 M	The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.
en_PP-OCRv4_mobile_rec	Inference Model/Training Model	70.39	4.81 / 0.75	16.10 / 5.31	6.8 M	The ultra-lightweight English recognition model, trained based on the PP-OCRv4 recognition model, supports the recognition of English letters and numbers.

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

👉Model List Details

* Chinese Recognition Model

Model	Model Download Link	Recognition Avg Accuracy(%)	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
PP-OCRv4_server_rec_doc	Inference Model/Training Model	81.53	6.65 / 2.38	32.92 / 32.92	74.7 M	PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the recognition capabilities for some traditional Chinese characters, Japanese, and special characters. The number of recognizable characters is over 15,000. In addition to the improvement in document-related text recognition, it also enhances the general text recognition capability.
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.74	4.82 / 1.20	16.74 / 4.64	10.6 M	The lightweight recognition model of PP-OCRv4 has high inference efficiency and can be deployed on various hardware devices, including edge devices.
PP-OCRv4_server_rec	Inference Model/Trained Model	80.61	6.58 / 2.43	33.17 / 33.17	71.2 M	The server-side model of PP-OCRv4 offers high inference accuracy and can be deployed on various types of servers.
PP-OCRv3_mobile_rec	Inference Model/Training Model	72.96	5.87 / 1.19	9.07 / 4.28	9.2 M	PP-OCRv3’s lightweight recognition model is designed for high inference efficiency and can be deployed on a variety of hardware devices, including edge devices.

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
ch_SVTRv2_rec	Inference Model/Training Model	68.81	8.08 / 2.74	50.17 / 42.50	73.9 M	SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan University's Visual and Learning Laboratory (FVL). It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the A list is 6% higher than that of PP-OCRv4.

Model	Model Download Link	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (M)	Introduction
ch_RepSVTR_rec	Inference Model/Training Model	65.07	5.93 / 1.62	20.73 / 7.32	22.1 M	The RepSVTR text recognition model is a mobile text recognition model based on SVTRv2. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the B list is 2.5% higher than that of PP-OCRv4, with the same inference speed.

* English Recognition Model

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

* Multilingual Recognition Model

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

Test Environment Description:

Performance Test Environment
Test Dataset:
- Document Image Orientation Classification Model: A self-built dataset using PaddleX, covering various scenarios such as ID cards and documents, containing 1000 images.
- Text Image Rectification Model: DocUNet.
- Layout Detection Model: A self-built layout detection dataset using PaddleOCR, including 500 images of common document types such as Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
- 3-Category Layout Detection Model: A self-built layout detection dataset using PaddleOCR, containing 1154 images of common document types such as Chinese and English papers, magazines, and research reports.
- 17-Category Region Detection Model: A self-built layout detection dataset using PaddleOCR, including 892 images of common document types such as Chinese and English papers, magazines, and research reports.
- 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 PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition.
- ch_RepSVTR_rec: Evaluation set B for PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition.
- 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 model pipelines provided by PaddleX can be quickly experienced. You can experience the effect of the seal text recognition pipeline on the community platform, or you can use the command line or Python locally to experience the effect of the seal text recognition pipeline.

2.1 Online Experience¶

You can experience the seal text recognition pipeline online by recognizing the demo images provided by the official platform, 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 methods 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 your private data. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.

2.2 Local Experience¶

❗ Before using the seal text recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX Installation Guide.

2.2.1 Command Line Experience¶

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

paddlex --pipeline seal_recognition \
    --input seal_text_det.png \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --device gpu:0 \
    --save_path ./output

The relevant parameter descriptions can be referred to in the parameter explanations of 2.1.2 Integration via Python Script.

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

👉Click to Expand

{'res': {'input_path': 'seal_text_det.png', 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 16, 'label': 'seal', 'score': 0.975531280040741, 'coordinate': [6.195526, 0.1579895, 634.3982, 628.84595]}]}, 'seal_res_list': [{'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': [array([[320,  38],
       ...,
       [315,  38]]), array([[461, 347],
       ...,
       [456, 346]]), array([[439, 445],
       ...,
       [434, 444]]), array([[158, 468],
       ...,
       [154, 466]])], 'text_det_params': {'limit_side_len': 736, 'limit_type': 'min', 'thresh': 0.2, 'box_thresh': 0.6, 'unclip_ratio': 0.5}, 'text_type': 'seal', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['天津君和缘商贸有限公司', '发票专用章', '吗繁物', '5263647368706'], 'rec_scores': array([0.9934051 , ..., 0.99139398]), 'rec_polys': [array([[320,  38],
       ...,
       [315,  38]]), array([[461, 347],
       ...,
       [456, 346]]), array([[439, 445],
       ...,
       [434, 444]]), array([[158, 468],
       ...,
       [154, 466]])], 'rec_boxes': array([], dtype=float64)}]}}

The explanation of the result parameters can be found in 2.1.2 Python Script Integration.

The visualized results are saved under save_path, and the visualized result of seal OCR is as follows:

2.2.2 Python Script Integration¶

The above command line is for quickly experiencing and viewing the effect. Generally, in a project, you often need to integrate through code. You can complete the quick 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="seal_recognition")

output = pipeline.predict(
    "seal_text_det.png",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
)
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")

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

(1) The seal recognition pipeline object was instantiated via create_pipeline(), with the specific parameters described as follows:

Parameter	Description	Type	Default Value
`pipeline`	The name of the pipeline or the path to the pipeline configuration file. If it is a pipeline name, it must be supported by PaddleX.	`str`	`None`
`config`	Specific configuration information for the pipeline (if set simultaneously with `pipeline`, it has higher priority than `pipeline`, and the pipeline name must be consistent with `pipeline`).	`dict[str, Any]`	`None`
`device`	The device used for pipeline inference. It supports specifying the specific card number of the GPU, 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 Seal Text Recognition pipeline object for inference prediction. This method will return 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 an image or PDF file, e.g., `/root/data/img.jpg`; URL link, e.g., the network URL of an image or PDF file: Example; Local directory, containing images to be predicted, e.g., `/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, e.g., `[numpy.ndarray, numpy.ndarray]`, `[\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"]`, `[\"/root/data1\", \"/root/data2\"]`	`None`
`device`	Inference device for the pipeline	`str\|None`	CPU: e.g., `cpu` for CPU inference; GPU: e.g., `gpu:0` for inference using the first GPU; NPU: e.g., `npu:0` for inference using the first NPU; XPU: e.g., `xpu:0` for inference using the first XPU; MLU: e.g., `mlu:0` for inference using the first MLU; DCU: e.g., `dcu:0` for inference using the first DCU; None: If set to `None`, the default value from the pipeline initialization 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 from the pipeline initialization 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 from the pipeline initialization 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 from the pipeline initialization will be used, initialized as `True`.	`None`
`layout_threshold`	Confidence threshold for layout detection; only scores above this threshold will be output	`float\|dict\|None`	float: Any float greater than `0` dict: Key is the int category ID, value is any float greater than `0` None: If set to `None`, the default value from the pipeline initialization will be used, initialized as `0.5`	`None`
`layout_nms`	Whether to use Non-Maximum Suppression (NMS) for layout detection post-processing	`bool\|None`	bool: `True` or `False`; None: If set to `None`, the default value from the pipeline initialization will be used, initialized as `True`.	`None`
`layout_unclip_ratio`	Expansion ratio of detection box edges; if not specified, the default value from the PaddleX official model configuration will be used	`float\|list\|None`	float: Any float greater than 0, e.g., 1.1, which means expanding the width and height of the detection box by 1.1 times while keeping the center unchanged list: e.g., [1.2, 1.5], which means expanding the width of the detection box by 1.2 times and the height by 1.5 times while keeping the center unchanged None: If set to `None`, the default value from the pipeline initialization will be used, initialized as 1.0
`layout_merge_bboxes_mode`	Merging mode for detection boxes in layout detection output; if not specified, the default value from the PaddleX official model configuration will be used	`string\|None`	large: When set to `large`, only the largest external box will be retained for overlapping detection boxes, and the internal overlapping boxes will be removed. small: When set to `small`, only the smallest internal box will be retained for overlapping detection boxes, and the external overlapping boxes will be removed. union: No filtering of boxes will be performed; both internal and external boxes will be retained. None: If set to `None`, the default value from the pipeline initialization will be used, initialized as `large`.	None
`seal_det_limit_side_len`	Side length limit for seal text detection	`int\|None`	int: Any integer greater than `0` None: If set to `None`, the default value from the pipeline initialization will be used, initialized as `736`	`None`
`seal_rec_score_thresh`	Text recognition threshold; text results with scores above this threshold will be retained	`float\|None`	float: Any float greater than `0` None: If set to `None`, the default value from the pipeline initialization will be used, initialized as `0.0`. This means no threshold is applied.	`None`

(3) Process the prediction results. The prediction result for each sample is of dict type and supports operations such as printing, saving as an image, and saving as a json file:

Method	Description	Parameter	Parameter Type	Parameter Description	Default Value
`print()`	Print results 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 for better readability, effective only 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, effective only when `format_json` is `True`	`False`
`save_to_json()`	Save results as a json file	`save_path`	`str`	The file path to save the results. When it is a directory, the saved file name will be consistent with the input file type	None
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data for better readability, effective only 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, effective only when `format_json` is `True`	`False`
`save_to_img()`	Save results as an image file	`save_path`	`str`	The file path to save the results, supports directory or file path	None

Calling the print() method will print the results to the terminal, and the explanations of the printed content are as follows:
- input_path: (str) The input path of the image to be predicted.
- model_settings: (Dict[str, bool]) The model parameters required for pipeline configuration.
  - use_doc_preprocessor: (bool) Controls whether to enable the document preprocessing sub-pipeline.
  - use_layout_detection: (bool) Controls whether to enable the layout detection sub-module.
- layout_det_res: (Dict[str, Union[List[numpy.ndarray], List[float]]]) The output result of the layout detection sub-module. Only exists when use_layout_detection=True.
  - input_path: (Union[str, None]) The image path accepted by the layout detection module. Saved as None when the input is a numpy.ndarray.
  - page_index: (Union[int, None]) Indicates the current page number of the PDF if the input is a PDF file; otherwise, it is None.
  - boxes: (List[Dict]) A list of detected layout seal regions, with each element containing the following fields:
    - cls_id: (int) The class ID of the detected seal region.
    - score: (float) The confidence score of the detected region.
    - coordinate: (List[float]) The coordinates of the four corners of the detection box, in the order of x1, y1, x2, y2, representing the x-coordinate of the top-left corner, the y-coordinate of the top-left corner, the x-coordinate of the bottom-right corner, and the y-coordinate of the bottom-right corner.
- seal_res_list: List[Dict] A list of seal text recognition results, with each element containing the following fields:
  - input_path: (Union[str, None]) The image path accepted by the seal text recognition pipeline. Saved as None when the input is a numpy.ndarray.
  - page_index: (Union[int, None]) Indicates the current page number of the PDF if the input is a PDF file; otherwise, it is None.
  - model_settings: (Dict[str, bool]) The model configuration parameters for the seal text recognition pipeline.
  - use_doc_preprocessor: (bool) Controls whether to enable the document preprocessing sub-pipeline.
  - use_textline_orientation: (bool) Controls whether to enable the text line orientation classification sub-module.
- doc_preprocessor_res: (Dict[str, Union[str, Dict[str, bool], int]]) The output result of the document preprocessing sub-pipeline. Only exists when use_doc_preprocessor=True.
  - input_path: (Union[str, None]) The image path accepted by the document preprocessing sub-pipeline. Saved as None when the input is a numpy.ndarray.
  - model_settings: (Dict) The model configuration parameters for the preprocessing sub-pipeline.
    - use_doc_orientation_classify: (bool) Controls whether to enable document orientation classification.
    - use_doc_unwarping: (bool) Controls whether to enable document unwarping.
  - angle: (int) The predicted result of document orientation classification. When enabled, it takes values [0, 1, 2, 3], corresponding to [0°, 90°, 180°, 270°]; when disabled, it is -1.
- dt_polys: (List[numpy.ndarray]) A list of polygon boxes for seal text detection. Each detection box is represented by a numpy array of multiple vertex coordinates, with the array shape being (n, 2).
- 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 handling 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 seal text detection, currently fixed as "seal".
- text_rec_score_thresh: (float) The filtering threshold for text recognition results.
- rec_texts: (List[str]) A list of text recognition results, containing only texts with confidence scores above text_rec_score_thresh.
- rec_scores: (List[float]) A list of confidence scores for text recognition, filtered by text_rec_score_thresh.
- rec_polys: (List[numpy.ndarray]) A list of text detection boxes filtered by confidence score, in the same format as dt_polys.
- rec_boxes: (numpy.ndarray) An array of rectangular bounding boxes for detection boxes; the seal recognition pipeline returns an empty array.
Calling the save_to_json() method will save the above content to the specified save_path. If a directory is specified, the saved 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 list format.
Calling the save_to_img() method will save the visualization results to the specified save_path. If a directory is specified, the saved path will be save_path/{your_img_basename}_seal_res_region1.{your_img_extension}. If a file is specified, it will be saved directly to that file. (The pipeline usually contains multiple result images, so it is not recommended to specify a specific file path directly, as multiple images will be overwritten, and only the last image will be retained.)
Additionally, you can obtain visualized images with results and prediction results through attributes, as follows:

Attribute	Description
`json`	Get the prediction results in `json` format.
`img`	Get the visualization results in `dict` format.

The prediction results obtained through the json attribute are of dict type, with content consistent with what is saved by calling the save_to_json() method.
The prediction results returned by the img attribute are of dict type. The keys are layout_det_res, seal_res_region1, and preprocessed_img, corresponding to three Image.Image objects: one for visualizing layout detection, one for visualizing seal text recognition results, and one for visualizing image preprocessing. If the image preprocessing sub-module is not used, preprocessed_img will not be included in the dictionary. If the layout region detection module is not used, layout_det_res will not be included.

Additionally, you can obtain the configuration file for the seal text recognition pipeline and load the configuration file for prediction. You can execute the following command to save the results in my_path:

paddlex --get_pipeline_config seal_recognition --save_path ./my_path

If you have obtained the configuration file, you can customize the settings for the seal text recognition pipeline by simply modifying 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/seal_recognition.yaml")
output = pipeline.predict("seal_text_det.png")
for res in output:
    res.print() ## 打印预测的结构化输出
    res.save_to_img("./output/") ## 保存可视化结果
    res.save_to_json("./output/") ## 保存预测结果的json文件

Note: The parameters in the configuration file are the pipeline initialization parameters. If you wish to change the initialization parameters of the seal text recognition 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 into your Python project, you can refer to the example code in 2.2.2 Python Script Method.

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

🚀 High-Performance Deployment: In practical 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 that aims to deeply optimize the performance of model inference and pre/post-processing, significantly speeding up the end-to-end process. For detailed high-performance deployment procedures, please refer to the PaddleX High-Performance Deployment Guide.

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

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

API Reference

For the main operations provided by the service:

The HTTP request method is POST.
The request body and response body are both 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	Description
`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	Description
`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

Obtain the seal text recognition result.

POST /seal-recognition

The attributes 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 file. For PDF files with more than 10 pages, only the content of the first 10 pages will be used.	Yes
`fileType`	`integer` \| `null`	The type of file. `0` indicates a PDF file, `1` indicates an image file. If this attribute is not present in the request body, the file type will be inferred from the URL.	No
`useDocOrientationClassify`	`boolean` \| `null`	Refer to the description of the `use_doc_orientation_classify` parameter in the `predict` method of the pipeline.	No
`useDocUnwarping`	`boolean` \| `null`	Refer to the description of the `use_doc_unwarping` parameter in the `predict` method of the pipeline.	No
`useLayoutDetection`	`boolean` \| `null`	Refer to the description of the `use_layout_detection` parameter in the `predict` method of the pipeline.	No
`layoutThreshold`	`number` \| `null`	Refer to the description of the `layout_threshold` parameter in the `predict` method of the pipeline.	No
`layoutNms`	`boolean` \| `null`	Refer to the description of the `layout_nms` parameter in the `predict` method of the pipeline.	No
`layoutUnclipRatio`	`number` \| `array` \| `null`	Refer to the description of the `layout_unclip_ratio` parameter in the `predict` method of the pipeline.	No
`layoutMergeBboxesMode`	`string` \| `null`	Refer to the description of the `layout_merge_bboxes_mode` parameter in the `predict` method of the pipeline.	No
`sealDetLimitSideLen`	`integer` \| `null`	Refer to the description of the `seal_det_limit_side_len` parameter in the `predict` method of the pipeline.	No
`sealDetLimitType`	`string` \| `null`	Refer to the description of the `seal_det_limit_type` parameter in the `predict` method of the pipeline.	No
`sealDetThresh`	`number` \| `null`	Refer to the description of the `seal_det_thresh` parameter in the `predict` method of the pipeline.	No
`sealDetBoxThresh`	`number` \| `null`	Refer to the description of the `seal_det_box_thresh` parameter in the `predict` method of the pipeline.	No
`sealDetUnclipRatio`	`number` \| `null`	Refer to the description of the `seal_det_unclip_ratio` parameter in the `predict` method of the pipeline.	No
`sealRecScoreThresh`	`number` \| `null`	Refer to the description of the `seal_rec_score_thresh` parameter in the `predict` method of the pipeline.	No

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

Name	Type	Meaning
`sealRecResults`	`object`	The seal text recognition result. 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 sealRecResults is an object with the following properties:

Name	Type	Meaning
`prunedResult`	`object`	A simplified version of the `res` field in the JSON representation generated by the `predict` method of the production object, where the `input_path` field is removed.
`outputImages`	`object` \| `null`	See the description of the `img` attribute in the result of the pipeline prediction. The images are 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 Invocation Example

Python

import base64
import requests

API_URL = "http://localhost:8080/seal-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["sealRecResults"]):
    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 directly on user devices, allowing devices to process data without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method based on your needs to integrate the model pipeline into subsequent AI applications.

4. Custom Development¶

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

4.1 Model Fine-Tuning¶

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

Scenario	Fine-Tuning Module	Fine-Tuning Reference Link
Inaccurate or missing seal position detection	Layout Detection Module	Link
Missing text detection	Text Detection Module	Link
Inaccurate text content	Text Recognition Module	Link
Inaccurate full-image rotation correction	Document Image Orientation Classification Module	Link
Inaccurate image distortion correction	Text Image Correction Module	Not supported for fine-tuning

4.2 Model Application¶

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

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

......
SubModules:
  LayoutDetection:
    module_name: layout_detection
    model_name: PP-DocLayout-L
    model_dir: null # 修改此处为微调后的版面检测模型权重的本地路径
    ...

SubPipelines:
  DocPreprocessor:
    ...
    SubModules:
      DocOrientationClassify:
        module_name: doc_text_orientation
        model_name: PP-LCNet_x1_0_doc_ori
        model_dir: null # 修改此处为微调后的文档图像方向分类模型权重的本地路径
    ...
    SubModules:
      TextDetection:
        module_name: seal_text_detection
        model_name: PP-OCRv4_server_seal_det
        model_dir: null # Modify this to the local path of the fine-tuned text detection model weights
        ...
        TextRecognition:
          module_name: text_recognition
          model_name: PP-OCRv4_server_rec
          model_dir: null # Modify this to the local path of the fine-tuned text recognition model weights
        ...

Then, refer to the command-line or Python script methods in 2.2 Local Experience to load the modified pipeline configuration file.

5. Multi-Hardware Support¶

PaddleX supports a variety of mainstream hardware devices, including 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 inference on the seal text recognition pipeline, the Python command would be:

paddlex --pipeline seal_recognition \
    --input seal_text_det.png \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --device npu:0 \
    --save_path ./output

If you wish to use the seal text recognition pipeline on a wider variety of hardware, please refer to the PaddleX Multi-Device Usage Guide.