docs/version3.x/pipeline_usage/seal_recognition.en.md
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 custom 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.
<b>The seal text recognition</b> 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.
In this pipeline, you can choose the model to use based on the benchmark data below.
<details> <summary> <b>Layout Region Detection Module (Optional):</b></summary>The inference time only includes the model inference time and does not include the time for pre- or post-processing. In the inference time columns labeled [Regular Mode / High-Performance Mode], the Regular Mode values correspond to the local
paddle_staticinference engine.
<details><summary> 👉Details of the Model List</summary>❗ Listed above are the <b>4 core models</b> that are the focus of the layout detection module, which supports a total of <b>13 full models</b>, including multiple models with pre-defined different categories, among which 9 models include the seal category. Apart from the 3 core models mentioned above, the remaining models are as follows:
<details><summary> 👉Details of the Model List</summary>❗ Listed above are the <b>6 core models</b> that are the focus of the text recognition module, which supports a total of <b>20 full models</b>, including multiple multi-language text recognition models, with the complete model list as follows:
<b>If you are more concerned with model accuracy, please choose a model with higher accuracy. If you are more concerned with inference speed, please choose a model with faster inference speed. If you are more concerned with model storage size, please choose a model with smaller storage size</b>.
Before using the seal text recognition pipeline locally, please ensure that you have completed the installation of the wheel package according to the installation tutorial. If you prefer to install dependencies selectively, please refer to the relevant instructions in the installation documentation. The corresponding dependency group for this pipeline is doc-parser. Once the installation is complete, you can experience it locally via the command line or integrate it with Python.
Please note: If you encounter issues such as the program becoming unresponsive, unexpected program termination, running out of memory resources, or extremely slow inference during execution, please try adjusting the configuration according to the documentation, such as disabling unnecessary features or using lighter-weight models.
You can quickly experience the seal_recognition pipeline effect with a single command:
paddleocr seal_recognition -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False
# Use --device to specify the use of GPU for model inference.
paddleocr seal_recognition -i ./seal_text_det.png --device gpu
The examples above use the local paddle_static inference engine by default. To run them, first install PaddlePaddle by following PaddlePaddle Framework Installation.
If you choose transformers as the inference engine, make sure the Transformers environment is configured by following Inference Engine and Configuration, and then run the following command:
# Use the transformers engine for inference
paddleocr seal_recognition -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--engine transformers
In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.
<b>Description:</b> Local path of image or PDF file, e.g., <code>/root/data/img.jpg</code>; <b>URL link</b>, e.g., network URL of image or PDF file: <a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png">Example</a>; <b>Local directory</b>, the directory should contain images to be predicted, e.g., local path: <code>/root/data/</code> (currently does not support prediction of PDF files in directories; PDF files must be specified with a specific file path).
</td> <td><code>str</code></td> <td></td> </tr> <tr> <td><code>save_path</code></td> <td> <b>Meaning:</b>Specify the path to save the inference results file.<b>Description:</b> If not set, the inference results will not be saved locally.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>doc_orientation_classify_model_name</code></td> <td> <b>Meaning:</b>The name of the document orientation classification model.<b>Description:</b> If not set, the default model in pipeline will be used.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>doc_orientation_classify_model_dir</code></td> <td><b>Meaning:</b>The directory path of the document orientation classification model.<b>Description:</b> If not set, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>doc_unwarping_model_name</code></td> <td> <b>Meaning:</b>The name of the text image unwarping model.<b>Description:</b> If not set, the default model in pipeline will be used.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>doc_unwarping_model_dir</code></td> <td> <b>Meaning:</b>The directory path of the text image unwarping model.<b>Description:</b> If not set, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>layout_detection_model_name</code></td> <td> <b>Meaning:</b>The name of the layout detection model.<b>Description:</b> If not set, the default model in pipeline will be used. </td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>layout_detection_model_dir</code></td> <td> <b>Meaning:</b>The directory path of the layout detection model.<b>Description:</b> If not set, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>seal_text_detection_model_name</code></td> <td><b>Meaning:</b>The name of the seal text detection model.<b>Description:</b> If not set, the pipeline's default model will be used.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>seal_text_detection_model_dir</code></td> <td><b>Meaning:</b>The directory path of the seal text detection model.<b>Description:</b> If not set, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>text_recognition_model_name</code></td> <td><b>Meaning:</b>Name of the text recognition model.<b>Description:</b> If not set, the default pipeline model is used.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>text_recognition_model_dir</code></td> <td><b>Meaning:</b>Directory path of the text recognition model.<b>Description:</b> If not set, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>text_recognition_batch_size</code></td> <td><b>Meaning:</b>Batch size for the text recognition model.<b>Description:</b> If not set, defaults to <code>1</code>.</td>
<td><code>int</code></td> <td></td> </tr> <tr> <td><code>use_doc_orientation_classify</code></td> <td><b>Meaning:</b>Whether to load and use document orientation classification module.<b>Description:</b> If not set, defaults to pipeline initialization value (<code>True</code>).</td>
<td><code>bool</code></td> <td></td> </tr> <tr> <td><code>use_doc_unwarping</code></td> <td><b>Meaning:</b>Whether to load and use text image correction module.<b>Description:</b> If not set, defaults to pipeline initialization value (<code>True</code>).</td>
<td><code>bool</code></td> <td></td> </tr> <tr> <td><code>use_layout_detection</code></td> <td> <b>Meaning:</b>Whether to load and use the layout detection module.<b>Description:</b> If not set, the parameter will be set to the value initialized in the pipeline, which is <code>True</code> by default.</td>
<td><code>bool</code></td> <td></td> </tr> <tr> <td><code>layout_threshold</code></td> <td><b>Meaning:</b>Score threshold for the layout model.<b>Description:</b> Any value between <code>0-1</code>. If not set, the default value is used, which is <code>0.5</code>.
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>layout_nms</code></td> <td><b>Meaning:</b>Whether to use Non-Maximum Suppression (NMS) as post-processing for layout detection.<b>Description:</b> If not set, the parameter will be set to the value initialized in the pipeline, which is set to <code>True</code> by default.</td>
<td><code>bool</code></td> <td></td> </tr> <tr> <td><code>layout_unclip_ratio</code></td> <td><b>Meaning:</b>Unclip ratio for detected boxes in layout detection model.<b>Description:</b> Any float > <code>0</code>. If not set, the default is <code>1.0</code>.
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>layout_merge_bboxes_mode</code></td> <td><b>Meaning:</b>The merging mode for the detection boxes output by the model in layout region detection.<b>Description:</b>
<ul> <li><b>large</b>: When set to "large", only the largest outer bounding box will be retained for overlapping bounding boxes, and the inner overlapping boxes will be removed;</li> <li><b>small</b>: When set to "small", only the smallest inner bounding boxes will be retained for overlapping bounding boxes, and the outer overlapping boxes will be removed;</li> <li><b>union</b>: No filtering of bounding boxes will be performed, and both inner and outer boxes will be retained;</li> </ul>If not set, the default is <code>large</code>. </td> <td><code>str</code></td> <td></td> </tr> <tr> <td><code>seal_det_limit_side_len</code></td> <td><b>Meaning:</b>Image side length limit for seal text detection.<b>Description:</b> Any integer > <code>0</code>. If not set, the default is <code>736</code>.
</td> <td><code>int</code></td> <td></td> </tr> <tr> <td><code>seal_det_limit_type</code></td> <td><b>Meaning:</b>Limit type for image side in seal text detection.<b>Description:</b> Supports <code>min</code> and <code>max</code>; <code>min</code> ensures shortest side ≥ <code>det_limit_side_len</code>, <code>max</code> ensures longest side ≤ <code>limit_side_len</code>. If not set, the default is <code>min</code>.
</td> <td><code>str</code></td> <td></td> </tr> <tr> <td><code>seal_det_thresh</code></td> <td><b>Meaning:</b>Pixel threshold. Pixels with scores above this value in the probability map are considered text.<b>Description:</b> Any float > <code>0</code>. If not set, the default is <code>0.2</code>.
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>seal_det_box_thresh</code></td> <td><b>Meaning:</b>Box threshold. Boxes with average pixel scores above this value are considered text regions.<b>Description:</b> Any float > <code>0</code>. If not set, the default is <code>0.6</code>.
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>seal_det_unclip_ratio</code></td> <td><b>Meaning:</b>Expansion ratio for seal text detection. Higher value means larger expansion area.<b>Description:</b> Any float > <code>0</code>. If not set, the default is <code>0.5</code>.
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>seal_rec_score_thresh</code></td> <td><b>Meaning:</b>Recognition score threshold. Text results above this value will be kept.<b>Description:</b> Any float > <code>0</code>. If not set, the default is <code>0.0</code> (no threshold).
</td> <td><code>float</code></td> <td></td> </tr> <tr> <td><code>device</code></td> <td><b>Meaning:</b>The device used for inference.<b>Description:</b> Support for specifying specific card numbers:
<ul> <li><b>CPU</b>: For example, <code>cpu</code> indicates using the CPU for inference.</li> <li><b>GPU</b>: For example, <code>gpu:0</code> indicates using the first GPU for inference.</li> <li><b>NPU</b>: For example, <code>npu:0</code> indicates using the first NPU for inference.</li> <li><b>XPU</b>: For example, <code>xpu:0</code> indicates using the first XPU for inference.</li> <li><b>MLU</b>: For example, <code>mlu:0</code> indicates using the first MLU for inference.</li> <li><b>DCU</b>: For example, <code>dcu:0</code> indicates using the first DCU for inference.</li> <li><b>MetaX GPU</b>: For example, <code>metax_gpu:0</code> indicates using the first MetaX GPU for inference.</li> <li><b>Iluvatar GPU</b>: For example, <code>iluvatar_gpu:0</code> indicates using the first Iluvatar GPU for inference.</li> </ul>If not set, the pipeline initialized value for this parameter will be used. During initialization, the local GPU device 0 will be preferred; if unavailable, the CPU device will be used. </td> <td><code>str</code></td> <td></td> </tr> <tr> <td><code>engine</code></td> <td><b>Meaning:</b> Inference engine. <b>Description:</b> Supports <code>None</code> (the default), <code>paddle</code>, <code>paddle_static</code>, <code>paddle_dynamic</code>, and <code>transformers</code>. When left as <code>None</code>, PaddleOCR preserves the behavior of earlier versions, which in most configurations is equivalent to <code>paddle</code>. For detailed descriptions, supported values, compatibility rules, and examples, see <a href="../inference_engine.en.md">Inference Engine and Configuration</a>.</td> <td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>enable_hpi</code></td> <td><b>Meaning:</b> Whether to enable high-performance inference.</td> <td><code>bool</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_tensorrt</code></td> <td><b>Meaning:</b> Whether to enable the TensorRT subgraph engine of Paddle Inference.<b>Description:</b> If the model does not support TensorRT acceleration, acceleration will not be used even if this flag is set.
For CUDA 11.8 versions of PaddlePaddle, the compatible TensorRT version is 8.x (x>=6). TensorRT 8.6.1.6 is recommended.
</td> <td><code>bool</code></td> <td><code>False</code></td> </tr> <tr> <td><code>precision</code></td> <td><b>Meaning:</b> Computation precision, such as <code>fp32</code> or <code>fp16</code>.</td> <td><code>str</code></td> <td><code>fp32</code></td> </tr> <tr> <td><code>enable_mkldnn</code></td> <td><b>Meaning:</b> Whether to enable MKL-DNN accelerated inference.<b>Description:</b> If MKL-DNN is unavailable or the model does not support MKL-DNN acceleration, acceleration will not be used even if this flag is set.
</td> <td><code>bool</code></td> <td><code>True</code></td> </tr> <tr> <td><code>mkldnn_cache_capacity</code></td> <td> <b>Meaning:</b> MKL-DNN cache capacity. </td> <td><code>int</code></td> <td><code>10</code></td> </tr> <tr> <td><code>cpu_threads</code></td> <td><b>Meaning:</b> Number of threads used for inference on CPU.</td> <td><code>int</code></td> <td><code>10</code></td> </tr> <tr> <td><code>paddlex_config</code></td> <td><b>Meaning:</b> Path to the PaddleX pipeline configuration file.</td> <td><code>str</code></td> <td></td> </tr> </tbody> </table> </details>After running, the results will be printed to the terminal, as follows:
{'res': {'input_path': './seal_text_det.png', 'model_settings': {'use_doc_preprocessor': True, 'use_layout_detection': True}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 16, 'label': 'seal', 'score': 0.975529670715332, 'coordinate': [6.191284, 0.16680908, 634.39325, 628.85345]}]}, '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, 'max_side_limit': 4000, '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([0.99340463, ..., 0.9916274 ]), '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 visualized results are saved under save_path, and the visualized result of seal OCR is as follows:
from paddleocr import SealRecognition
pipeline = SealRecognition(
use_doc_orientation_classify=False, # Set whether to use document orientation classification model
use_doc_unwarping=False, # Set whether to use document image unwarping module
)
# ocr = SealRecognition(device="gpu") # Specify GPU for model inference
output = pipeline.predict("./seal_text_det.png")
for res in output:
res.print() ## Print structured prediction results
res.save_to_img("./output/")
res.save_to_json("./output/")
The example above uses the local paddle_static inference engine by default. To run it, first install PaddlePaddle by following PaddlePaddle Framework Installation.
If you choose transformers as the inference engine, make sure the Transformers environment is configured by following Inference Engine and Configuration, and then run the following code:
from paddleocr import SealRecognition
pipeline = SealRecognition(
engine="transformers",
)
# ocr = SealRecognition(device="gpu") # Specify GPU for model inference
output = pipeline.predict("./seal_text_det.png")
for res in output:
res.print() ## Print structured prediction results
res.save_to_img("./output/")
res.save_to_json("./output/")
In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.
In the above Python script, the following steps were executed:
(1) Instantiate a pipeline object for seal text recognition using the SealRecognition() class, with specific parameter descriptions as follows:
<table> <thead> <tr> <th>Parameter</th> <th>Description</th> <th>Type</th> <th>Default Value</th> </tr> </thead> <tbody> <tr> <td><code>doc_orientation_classify_model_name</code></td> <td><b>Meaning:</b>Name of the document orientation classification model.<b>Description:</b> If set to <code>None</code>, the pipeline default model is used.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>doc_orientation_classify_model_dir</code></td> <td><b>Meaning:</b>Directory path of the document orientation classification model.<b>Description:</b> If set to <code>None</code>, the official model will be downloaded.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>doc_unwarping_model_name</code></td> <td><b>Meaning:</b>Name of the document unwarping model.<b>Description:</b> If set to <code>None</code>, the pipeline default model is used.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>doc_unwarping_model_dir</code></td> <td><b>Meaning:</b>Directory path of the document unwarping model.<b>Description:</b> If set to <code>None</code>, the official model will be downloaded.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_detection_model_name</code></td> <td><b>Meaning:</b>Name of the layout detection model.<b>Description:</b> If set to <code>None</code>, the pipeline default model is used.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_detection_model_dir</code></td> <td><b>Meaning:</b>Directory path of the layout detection model.<b>Description:</b> If set to <code>None</code>, the official model will be downloaded.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_text_detection_model_name</code></td> <td><b>Meaning:</b>Name of the seal text detection model.<b>Description:</b> If set to <code>None</code>, the default model will be used.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>seal_text_detection_model_dir</code></td> <td><b>Meaning:</b>Directory of the seal text detection model.<b>Description:</b> If set to <code>None</code>, the official model will be downloaded.</td>
<td><code>str</code></td> <td></td> </tr> <tr> <td><code>text_recognition_model_name</code></td> <td><b>Meaning:</b>Name of the text recognition model.<b>Description:</b> If set to <code>None</code>, the pipeline default model is used.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>text_recognition_model_dir</code></td> <td><b>Meaning:</b>Directory path of the text recognition model.<b>Description:</b> If set to <code>None</code>, the official model will be downloaded.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>text_recognition_batch_size</code></td> <td><b>Meaning:</b>Batch size for the text recognition model.<b>Description:</b> If set to <code>None</code>, the default batch size is <code>1</code>.</td>
<td><code>int|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_doc_orientation_classify</code></td> <td><b>Meaning:</b>Whether to enable the document orientation classification module.<b>Description:</b> If set to <code>None</code>, the default value is <code>True</code>.</td>
<td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_doc_unwarping</code></td> <td><b>Meaning:</b>Whether to enable the document image unwarping module.<b>Description:</b> If set to <code>None</code>, the default value is <code>True</code>.</td>
<td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_layout_detection</code></td> <td><b>Meaning:</b>Whether to load and use the layout detection module.<b>Description:</b> If set to <code>None</code>, the parameter will be set to the value initialized in the pipeline, which is <code>True</code> by default.</td>
<td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_threshold</code></td> <td><b>Meaning:</b>Score threshold for the layout model.<b>Description:</b>
<ul> <li><b>float</b>: Any float between <code>0-1</code>;</li> <li><b>dict</b>: <code>{0:0.1}</code> where the key is the class ID and the value is the threshold for that class;</li> <li><b>None</b>: If set to <code>None</code>, uses the pipeline default of <code>0.5</code>.</li> </ul> </td> <td><code>float|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_nms</code></td> <td><b>Meaning:</b>Whether to use Non-Maximum Suppression (NMS) as post-processing for layout detection.<b>Description:</b> If set to <code>None</code>, the parameter will be set to the value initialized in the pipeline, which is set to <code>True</code> by default.</td>
<td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_unclip_ratio</code></td> <td><b>Meaning:</b>Expansion ratio for the bounding boxes from the layout detection model.<b>Description:</b>
<ul> <li><b>float</b>: Any float greater than <code>0</code>;</li> <li><b>Tuple[float,float]</b>: Expansion ratios in horizontal and vertical directions;</li> <li><b>dict</b>: A dictionary with <b>int</b> keys representing <code>cls_id</code>, and <b>tuple</b> values, e.g., <code>{0: (1.1, 2.0)}</code> means width is expanded 1.1× and height 2.0× for class 0 boxes;</li> <li><b>None</b>: If set to <code>None</code>, uses the pipeline default of <code>1.0</code>.</li> </ul> </td> <td><code>float|Tuple[float,float]|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_merge_bboxes_mode</code></td> <td><b>Meaning:</b>Filtering method for overlapping boxes in layout detection.<b>Description:</b>
<ul> <li><b>str</b>: Options include <code>large</code>, <code>small</code>, and <code>union</code> to retain the larger box, smaller box, or both;</li> <li><b>dict</b>: A dictionary with <b>int</b> keys representing <code>cls_id</code>, and <b>str</b> values, e.g., <code>{0: "large", 2: "small"}</code> means using different modes for different classes;</li> <li><b>None</b>: If set to <code>None</code>, uses the pipeline default value <code>large</code>.</li> </ul> </td> <td><code>str|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_limit_side_len</code></td> <td><b>Meaning:</b>Image side length limit for seal text detection.<b>Description:</b>
<ul> <li><b>int</b>: Any integer greater than <code>0</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>736</code>.</li> </ul> </td> <td><code>int|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_limit_type</code></td> <td><b>Meaning:</b>Limit type for seal text detection image side length.<b>Description:</b>
<ul> <li><b>str</b>: Supports <code>min</code> and <code>max</code>. <code>min</code> ensures the shortest side is no less than <code>det_limit_side_len</code>, while <code>max</code> ensures the longest side is no greater than <code>limit_side_len</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>min</code>.</li> </ul> </td> <td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_thresh</code></td> <td><b>Meaning:</b>Pixel threshold for detection. Pixels with scores greater than this value in the probability map are considered text pixels.<b>Description:</b>
<ul> <li><b>float</b>: Any float greater than <code>0</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>0.2</code>.</li> </ul> </td> <td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_box_thresh</code></td> <td><b>Meaning:</b>Bounding box threshold. If the average score of all pixels inside a detection box exceeds this threshold, it is considered a text region.<b>Description:</b>
<ul> <li><b>float</b>: Any float greater than <code>0</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>0.6</code>.</li> </ul> </td> <td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_unclip_ratio</code></td> <td><b>Meaning:</b>Expansion ratio for seal text detection. The larger the value, the larger the expanded area.<b>Description:</b>
<ul> <li><b>float</b>: Any float greater than <code>0</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>0.5</code>.</li> </ul> </td> <td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_rec_score_thresh</code></td> <td><b>Meaning:</b>Score threshold for seal text recognition. Text results with scores above this threshold will be retained.<b>Description:</b>
<ul> <li><b>float</b>: Any float greater than <code>0</code>;</li> <li><b>None</b>: If set to <code>None</code>, the default value is <code>0.0</code> (no threshold).</li> </ul> </td> <td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>device</code></td> <td><b>Meaning:</b>Device used for inference.<b>Description:</b> Supports specifying device ID:
<ul> <li><b>CPU</b>: e.g., <code>cpu</code> means using CPU for inference;</li> <li><b>GPU</b>: e.g., <code>gpu:0</code> means using GPU 0;</li> <li><b>NPU</b>: e.g., <code>npu:0</code> means using NPU 0;</li> <li><b>XPU</b>: e.g., <code>xpu:0</code> means using XPU 0;</li> <li><b>MLU</b>: e.g., <code>mlu:0</code> means using MLU 0;</li> <li><b>DCU</b>: e.g., <code>dcu:0</code> means using DCU 0;</li> <li><b>MetaX GPU</b>: e.g., <code>metax_gpu:0</code> means using MetaX GPU 0;</li> <li><b>Iluvatar GPU</b>: e.g., <code>iluvatar_gpu:0</code> means using Iluvatar GPU 0;</li> <li><b>None</b>: If set to <code>None</code>, the pipeline initialized value for this parameter will be used. During initialization, the local GPU device 0 will be preferred; if unavailable, the CPU device will be used.</li> </ul> </td> <td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>engine</code></td> <td><b>Meaning:</b> Inference engine. <b>Description:</b> Supports <code>None</code> (the default), <code>paddle</code>, <code>paddle_static</code>, <code>paddle_dynamic</code>, and <code>transformers</code>. When left as <code>None</code>, PaddleOCR preserves the behavior of earlier versions, which in most configurations is equivalent to <code>paddle</code>. For detailed descriptions, supported values, compatibility rules, and examples, see <a href="../inference_engine.en.md">Inference Engine and Configuration</a>.</td> <td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>engine_config</code></td> <td><b>Meaning:</b> Inference-engine configuration. <b>Description:</b> Recommended together with <code>engine</code>. For supported fields, compatibility rules, and examples, see <a href="../inference_engine.en.md">Inference Engine and Configuration</a>.</td> <td><code>dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>enable_hpi</code></td> <td><b>Meaning:</b> Whether to enable high-performance inference.</td> <td><code>bool</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_tensorrt</code></td> <td><b>Meaning:</b> Whether to enable the TensorRT subgraph engine of Paddle Inference.<b>Description:</b> If the model does not support TensorRT acceleration, acceleration will not be used even if this flag is set.
For CUDA 11.8 versions of PaddlePaddle, the compatible TensorRT version is 8.x (x>=6). TensorRT 8.6.1.6 is recommended.
</td> <td><code>bool</code></td> <td><code>False</code></td> </tr> <tr> <td><code>precision</code></td> <td><b>Meaning:</b> Computation precision, such as <code>"fp32"</code> or <code>"fp16"</code>.</td> <td><code>str</code></td> <td><code>"fp32"</code></td> </tr> <tr> <td><code>enable_mkldnn</code></td> <td><b>Meaning:</b> Whether to enable MKL-DNN accelerated inference.<b>Description:</b> If MKL-DNN is unavailable or the model does not support MKL-DNN acceleration, acceleration will not be used even if this flag is set.
</td> <td><code>bool</code></td> <td><code>True</code></td> </tr> <tr> <td><code>mkldnn_cache_capacity</code></td> <td> <b>Meaning:</b> MKL-DNN cache capacity. </td> <td><code>int</code></td> <td><code>10</code></td> </tr> <tr> <td><code>cpu_threads</code></td> <td><b>Meaning:</b> Number of threads used for inference on CPU.</td> <td><code>int</code></td> <td><code>10</code></td> </tr> <tr> <td><code>paddlex_config</code></td> <td><b>Meaning:</b> Path to the PaddleX pipeline configuration file.</td> <td><code>str|None</code></td> <td><code>None</code></td> </tr> </tbody> </table>(2) Call the <code>predict()</code> method of the Seal Text Recognition pipeline object for inference prediction. This method will return a <code>generator</code>. Below are the parameters and their descriptions for the <code>predict()</code> method:
<table> <thead> <tr> <th>Parameter</th> <th>Parameter Description</th> <th>Parameter Type</th> <th>Default Value</th> </tr> </thead> <tr> <td><code>input</code></td> <td><b>Meaning:</b>Input data to be predicted. Required.<b>Description:</b> Supports multiple types:
<ul> <li><b>Python Var</b>: Image data represented by <code>numpy.ndarray</code>;</li> <li><b>str</b>: Local path of an image or PDF file, e.g., <code>/root/data/img.jpg</code>; <b>URL link</b>, e.g., the network URL of an image or PDF file: <a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png">Example</a>; <b>Local directory</b>, containing images to be predicted, e.g., <code>/root/data/</code> (currently does not support prediction of PDF files in directories; PDF files must be specified with an exact file path);</li> <li><b>list</b>: Elements of the list must be of the above types, e.g., <code>[numpy.ndarray, numpy.ndarray]</code>, <code>[\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"]</code>, <code>[\"/root/data1\", \"/root/data2\"]</code>.</li> </ul> </td> <td><code>Python Var|str|list</code></td> <td></td> </tr> <tr> <td><code>use_doc_orientation_classify</code></td> <td><b>Meaning:</b>Whether to use the document orientation classification module during inference.</td> <td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_doc_unwarping</code></td> <td><b>Meaning:</b>Whether to use the text image correction module during inference.</td> <td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>use_layout_detection</code></td> <td> <b>Meaning:</b>Whether to use the layout detection module during inference. </td> <td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_threshold</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_nms</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>bool|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_unclip_ratio</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|Tuple[float,float]|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>layout_merge_bboxes_mode</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>str|dict|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_limit_side_len</code></td> <td>Same meaning as the instantiation parameters. If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td> <td><code>int|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_limit_type</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>str|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_thresh</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_box_thresh</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_det_unclip_ratio</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|None</code></td> <td><code>None</code></td> </tr> <tr> <td><code>seal_rec_score_thresh</code></td> <td><b>Meaning:</b>Same meaning as the instantiation parameters.<b>Description:</b> If set to <code>None</code>, the instantiation value is used; otherwise, this parameter takes precedence.</td>
<td><code>float|None</code></td> <td><code>None</code></td> </tr> </table> </details>(3) Process the prediction results. The prediction result for each sample is of <code>dict</code> type and supports operations such as printing, saving as an image, and saving as a <code>json</code> file:
<table> <thead> <tr> <th>Method</th> <th>Description</th> <th>Parameter</th> <th>Parameter Type</th> <th>Parameter Description</th> <th>Default Value</th> </tr> </thead> <tr> <td rowspan="3"><code>print()</code></td> <td rowspan="3">Print results to the terminal</td> <td><code>format_json</code></td> <td><code>bool</code></td> <td>Whether to format the output content using <code>JSON</code> indentation.</td> <td><code>True</code></td> </tr> <tr> <td><code>indent</code></td> <td><code>int</code></td> <td>Specify the indentation level to beautify the output <code>JSON</code> data for better readability, effective only when <code>format_json</code> is <code>True</code>.</td> <td>4</td> </tr> <tr> <td><code>ensure_ascii</code></td> <td><code>bool</code></td> <td>Control whether to escape non-<code>ASCII</code> characters to <code>Unicode</code>. When set to <code>True</code>, all non-<code>ASCII</code> characters will be escaped; <code>False</code> will retain the original characters, effective only when <code>format_json</code> is <code>True</code>.</td> <td><code>False</code></td> </tr> <tr> <td rowspan="3"><code>save_to_json()</code></td> <td rowspan="3">Save results as a json file</td> <td><code>save_path</code></td> <td><code>str</code></td> <td>The file path to save the results. When it is a directory, the saved file name will be consistent with the input file type.</td> <td>None</td> </tr> <tr> <td><code>indent</code></td> <td><code>int</code></td> <td>Specify the indentation level to beautify the output <code>JSON</code> data for better readability, effective only when <code>format_json</code> is <code>True</code>.</td> <td>4</td> </tr> <tr> <td><code>ensure_ascii</code></td> <td><code>bool</code></td> <td>Control whether to escape non-<code>ASCII</code> characters to <code>Unicode</code>. When set to <code>True</code>, all non-<code>ASCII</code> characters will be escaped; <code>False</code> will retain the original characters, effective only when <code>format_json</code> is <code>True</code>.</td> <td><code>False</code></td> </tr> <tr> <td><code>save_to_img()</code></td> <td>Save results as an image file</td> <td><code>save_path</code></td> <td><code>str</code></td> <td>The file path to save the results, supports directory or file path.</td> <td>None</td> </tr> </table> <ul> <li>Calling the<code>print()</code> method will print the results to the terminal, and the explanations of the printed content are as follows: <ol start="1" type="1"> <li><code>input_path</code>: <code>(str)</code> The input path of the image to be predicted.</li> <li><code>model_settings</code>: <code>(Dict[str, bool])</code> The model parameters required for pipeline configuration. <ol> <li><code>use_doc_preprocessor</code>: <code>(bool)</code> Controls whether to enable the document preprocessing sub-pipeline.</li> <li><code>use_layout_detection</code>: <code>(bool)</code> Controls whether to enable the layout detection sub-module.</li> </ol> </li> <li><code>layout_det_res</code>: <code>(Dict[str, Union[List[numpy.ndarray], List[float]]])</code> The output result of the layout detection sub-module. Only exists when <code>use_layout_detection=True</code> <ol> <li><code>input_path</code>: <code>(Union[str, None])</code> The image path accepted by the layout detection module. Saved as <code>None</code> when the input is a <code>numpy.ndarray</code>.</li> <li><code>page_index</code>: <code>(Union[int, None])</code> Indicates the current page number of the PDF if the input is a PDF file; otherwise, it is<code>None</code>.</li> <li><code>boxes</code>: <code>(List[Dict])</code> A list of detected layout seal regions, with each element containing the following fields:</li> <ul> <li><code>cls_id</code>: <code>(int)</code> The class ID of the detected seal region.</li> <li><code>score</code>: <code>(float)</code> The confidence score of the detected region.</li> <li><code>coordinate</code>: <code>(List[float])</code> 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.</li> </ul> </ol> </li> <li><code>seal_res_list</code>: <code>List[Dict]</code> A list of seal text recognition results, with each element containing the following fields: <ol> <li><code>input_path</code>: <code>(Union[str, None])</code> The image path accepted by the seal text recognition pipeline. Saved as <code>None</code> when the input is a <code>numpy.ndarray</code>.</li> <li><code>page_index</code>: <code>(Union[int, None])</code> Indicates the current page number of the PDF if the input is a PDF file; otherwise, it is <code>None</code>.</li> <li><code>model_settings</code>: <code>(Dict[str, bool])</code> The model configuration parameters for the seal text recognition pipeline.</li> <ul> <li><code>use_doc_preprocessor</code>: <code>(bool)</code> Controls whether to enable the document preprocessing sub-pipeline.</li> <li><code>use_textline_orientation</code>: <code>(bool)</code> Controls whether to enable the text line orientation classification sub-module.</li> </ul> </ol> </li> <li><code>doc_preprocessor_res</code>: <code>(Dict[str, Union[str, Dict[str, bool], int]])</code> The output result of the document preprocessing sub-pipeline. Only exists when <code>use_doc_preprocessor=True</code>. <ol> <li><code>input_path</code>: <code>(Union[str, None])</code> The image path accepted by the document preprocessing sub-pipeline. Saved as <code>None</code> when the input is a <code>numpy.ndarray</code>.</li> <li><code>model_settings</code>: <code>(Dict[str, bool])</code> The model configuration parameters for the preprocessing sub-pipeline.</li> <ul> <li><code>use_doc_orientation_classify</code>: <code>(bool)</code> Controls whether to enable the document orientation classification sub-module.</li> <li><code>use_doc_unwarping</code>: <code>(bool)</code> Controls whether to enable the document unwarping sub-module.</li> </ul> <li><code>angle</code>: <code>(int)</code> 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.</li> </ol> </li> <li><code>dt_polys</code>: <code>(List[numpy.ndarray])</code> A list of polygon boxes for text detection. Each box is represented by a numpy array of shape (n, 2), where n is the number of vertices.</li> <li><code>dt_scores</code>: <code>(List[float])</code> A list of confidence scores for text detection boxes.</li> <li><code>dt_scores</code>: <code>(List[float])</code> A list of confidence scores for text detection boxes.</li> <li><code>text_det_params</code>: <code>(Dict[str, Dict[str, int, float]])</code> Configuration parameters for the text detection module. <ol> <li><code>limit_side_len</code>: <code>(int)</code> The side length limit value during image preprocessing.</li> <li><code>limit_type</code>: <code>(str)</code> The type of side length limit, which can be "min" or "max".</li> <li><code>thresh</code>: <code>(float)</code> The confidence threshold for text pixel classification.</li> <li><code>box_thresh</code>: <code>(float)</code> The confidence threshold for text detection boxes.</li> <li><code>unclip_ratio</code>: <code>(float)</code> The expansion ratio for text detection boxes.</li> <li><code>text_type</code>: <code>(str)</code> The type of text to be detected, which is fixed as "seal" for seal text detection.</li> </ol> </li> <li><code>text_rec_score_thresh</code>: <code>(float)</code> The filtering threshold for text recognition results.</li> <li><code>rec_texts</code>: <code>(List[str])</code> A list of text recognition results, containing only texts with confidence scores above <code>text_rec_score_thresh</code>.</li> <li><code>rec_scores</code>: <code>(List[float])</code> A list of confidence scores for text recognition, filtered by <code>text_rec_score_thresh</code>.</li> <li><code>rec_polys</code>: <code>(List[numpy.ndarray])</code> A list of text detection boxes filtered by confidence score, in the same format as <code>dt_polys</code>.</li> <li><code>rec_boxes</code>: <code>(numpy.ndarray)</code> 检An array of rectangular bounding boxes for detection boxes; the seal recognition pipeline returns an empty array.</li> </ol> </li> <li>Calling the <code>save_to_json()</code> method will save the above content to the specified <code>save_path</code>. If a directory is specified, the saved path will be <code>save_path/{your_img_basename}_res.json</code>. If a file is specified, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, <code>numpy.array</code> types will be converted to list format.</li> <li>Calling the <code>save_to_img()</code> method will save the visualization results to the specified <code>save_path</code>. If a directory is specified, the saved path will be <code>save_path/{your_img_basename}_seal_res_region1.{your_img_extension}</code>. 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.)</li>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 Python Script Method.
In addition, PaddleOCR also provides three other deployment methods, which are detailed as follows:
🚀 High-Performance Inference: In real-world production environments, many applications have stringent performance requirements for deployment strategies, especially in terms of response speed, to ensure efficient system operation and a smooth user experience. To address this, PaddleOCR offers high-performance inference capabilities aimed at deeply optimizing the performance of model inference and pre/post-processing, thereby significantly accelerating the end-to-end process. For detailed high-performance inference procedures, please refer to High-Performance Inference.
☁️ Service Deployment: Service deployment is a common form of deployment in real-world production environments. By encapsulating inference functionality into a service, clients can access these services via network requests to obtain inference results. For detailed production service deployment procedures, please refer to Serving.
Below are the API references for basic serving deployment and multi-language service invocation examples:
<details><summary>API Reference</summary> <p>For the main operations provided by the service:</p> <ul> <li>The HTTP request method is POST.</li> <li>The request body and response body are both JSON data (JSON objects).</li> <li>When the request is processed successfully, the response status code is <code>200</code>, and the attributes of the response body are as follows:</li> </ul> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>logId</code></td> <td><code>string</code></td> <td>The UUID of the request.</td> </tr> <tr> <td><code>errorCode</code></td> <td><code>integer</code></td> <td>Error code. Fixed as <code>0</code>.</td> </tr> <tr> <td><code>errorMsg</code></td> <td><code>string</code></td> <td>Error message. Fixed as <code>"Success"</code>.</td> </tr> <tr> <td><code>result</code></td> <td><code>object</code></td> <td>The result of the operation.</td> </tr> </tbody> </table> <ul> <li>When the request is not processed successfully, the attributes of the response body are as follows:</li> </ul> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>logId</code></td> <td><code>string</code></td> <td>The UUID of the request.</td> </tr> <tr> <td><code>errorCode</code></td> <td><code>integer</code></td> <td>Error code. Same as the response status code.</td> </tr> <tr> <td><code>errorMsg</code></td> <td><code>string</code></td> <td>Error message.</td> </tr> </tbody> </table> <p>The main operations provided by the service are as follows:</p> <ul> <li><b><code>infer</code></b></li> </ul> <p>Obtain the seal text recognition result.</p> <p><code>POST /seal-recognition</code></p> <ul> <li>The attributes of the request body are as follows:</li> </ul> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Description</th> <th>Required</th> </tr> </thead> <tbody> <tr> <td><code>file</code></td> <td><code>string</code></td> <td>The URL of an image or PDF file accessible by the server, or the Base64-encoded content of the file. By default, for PDF files exceeding 10 pages, only the content of the first 10 pages will be processed.To remove the page limit, please add the following configuration to the pipeline configuration file:
<pre><code>Serving: extra: max_num_input_imgs: null </code></pre></td> <td>Yes</td> </tr> <tr> <td><code>fileType</code></td> <td><code>integer</code> | <code>null</code></td> <td>The type of file. <code>0</code> indicates a PDF file, <code>1</code> indicates an image file. If this attribute is not present in the request body, the file type will be inferred from the URL.</td> <td>No</td> </tr> <tr> <td><code>useDocOrientationClassify</code></td> <td><code>boolean</code> | <code>null</code></td> <td>Please refer to the description of the <code>use_doc_orientation_classify</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>useDocUnwarping</code></td> <td><code>boolean</code> | <code>null</code></td> <td>Please refer to the description of the <code>use_doc_unwarping</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>useLayoutDetection</code></td> <td><code>boolean</code> | <code>null</code></td> <td>Please refer to the description of the <code>use_layout_detection</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>layoutThreshold</code></td> <td><code>number</code> | <code>null</code></td> <td>Please refer to the description of the <code>layout_threshold</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>layoutNms</code></td> <td><code>boolean</code> | <code>null</code></td> <td>Please refer to the description of the <code>layout_nms</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>layoutUnclipRatio</code></td> <td><code>number</code> | <code>array</code> | <code>null</code></td> <td>Please refer to the description of the <code>layout_unclip_ratio</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>layoutMergeBboxesMode</code></td> <td><code>string</code> | <code>null</code></td> <td>Please refer to the description of the <code>layout_merge_bboxes_mode</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealDetLimitSideLen</code></td> <td><code>integer</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_det_limit_side_len</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealDetLimitType</code></td> <td><code>string</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_det_limit_type</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealDetThresh</code></td> <td><code>number</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_det_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealDetBoxThresh</code></td> <td><code>number</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_det_box_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealDetUnclipRatio</code></td> <td><code>number</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_det_unclip_ratio</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>sealRecScoreThresh</code></td> <td><code>number</code> | <code>null</code></td> <td>Please refer to the description of the <code>seal_rec_score_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td> <td>No</td> </tr> <tr> <td><code>visualize</code></td> <td><code>boolean</code> | <code>null</code></td> <td> Whether to return the final visualization image and intermediate images during the processing. <ul style="margin: 0 0 0 1em; padding-left: 0em;"> <li>If <code>true</code> is provided: return images.</li> <li>If <code>false</code> is provided: do not return any images.</li> <li>If this parameter is omitted from the request body, or if <code>null</code> is explicitly passed, the behavior will follow the value of <code>Serving.visualize</code> in the pipeline configuration.</li> </ul>For example, adding the following setting to the pipeline config file:
<pre><code>Serving: visualize: False </code></pre>will disable image return by default. This behavior can be overridden by explicitly setting the <code>visualize</code> parameter in the request.
If neither the request body nor the configuration file is set (If <code>visualize</code> is set to <code>null</code> in the request and not defined in the configuration file), the image is returned by default.
</td> <td>No</td> </tr> </tbody> </table> <ul> <li>When the request is processed successfully, the <code>result</code> in the response body has the following properties:</li> </ul> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Meaning</th> </tr> </thead> <tbody> <tr> <td><code>sealRecResults</code></td> <td><code>object</code></td> <td>The seal text recognition result. The array length is 1 (for image input) or the actual number of document pages processed (for PDF input). For PDF input, each element in the array represents the result of each page actually processed in the PDF file.</td> </tr> <tr> <td><code>dataInfo</code></td> <td><code>object</code></td> <td>Information about the input data.</td> </tr> </tbody> </table> <p>Each element in <code>sealRecResults</code> is an <code>object</code> with the following properties:</p> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Meaning</th> </tr> </thead> <tbody> <tr> <td><code>prunedResult</code></td> <td><code>object</code></td> <td>A simplified version of the <code>res</code> field in the JSON representation generated by the <code>predict</code> method of the production object, where the <code>input_path</code> and the <code>page_index</code> fields are removed.</td> </tr> <tr> <td><code>outputImages</code></td> <td><code>object</code> | <code>null</code></td> <td>See the description of the <code>img</code> attribute of the result of the pipeline prediction. The images are in JPEG format and encoded in Base64.</td> </tr> <tr> <td><code>inputImage</code></td> <td><code>string</code> | <code>null</code></td> <td>The input image. The image is in JPEG format and encoded in Base64.</td> </tr> </tbody> </table></details> <details><summary>Multi-language Service Invocation Example</summary> <details> <summary>Python</summary> <pre><code class="language-python">import base64 import requests API_URL = "http://localhost:8080/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}") </code></pre></details> <details><summary>C++</summary> <pre><code class="language-cpp">#include <iostream> #include <fstream> #include <vector> #include <string> #include "cpp-httplib/httplib.h" // https://github.com/Huiyicc/cpp-httplib #include "nlohmann/json.hpp" // https://github.com/nlohmann/json #include "base64.hpp" // https://github.com/tobiaslocker/base64 int main() { httplib::Client client("localhost", 8080); const std::string filePath = "./demo.jpg"; std::ifstream file(filePath, std::ios::binary | std::ios::ate); if (!file) { std::cerr << "Error opening file: " << filePath << std::endl; return 1; } std::streamsize size = file.tellg(); file.seekg(0, std::ios::beg); std::vector<char> buffer(size); if (!file.read(buffer.data(), size)) { std::cerr << "Error reading file." << std::endl; return 1; } std::string bufferStr(buffer.data(), static_cast<size_t>(size)); std::string encodedFile = base64::to_base64(bufferStr); nlohmann::json jsonObj; jsonObj["file"] = encodedFile; jsonObj["fileType"] = 1; auto response = client.Post("/seal-recognition", jsonObj.dump(), "application/json"); if (response && response->status == 200) { nlohmann::json jsonResponse = nlohmann::json::parse(response->body); auto result = jsonResponse["result"]; if (!result.is_object() || !result["sealRecResults"].is_array()) { std::cerr << "Unexpected response format." << std::endl; return 1; } for (size_t i = 0; i < result["sealRecResults"].size(); ++i) { auto res = result["sealRecResults"][i]; if (res.contains("prunedResult")) { std::cout << "Recognized seal result: " << res["prunedResult"].dump() << std::endl; } if (res.contains("outputImages") && res["outputImages"].is_object()) { for (auto& [imgName, imgData] : res["outputImages"].items()) { std::string outputPath = imgName + "_" + std::to_string(i) + ".jpg"; std::string decodedImage = base64::from_base64(imgData.get<std::string>()); std::ofstream outFile(outputPath, std::ios::binary); if (outFile.is_open()) { outFile.write(decodedImage.c_str(), decodedImage.size()); outFile.close(); std::cout << "Saved image: " << outputPath << std::endl; } else { std::cerr << "Failed to write image: " << outputPath << std::endl; } } } } } else { std::cerr << "Request failed." << std::endl; if (response) { std::cerr << "HTTP status: " << response->status << std::endl; std::cerr << "Response body: " << response->body << std::endl; } return 1; } return 0; } </code></pre></details> <details><summary>Java</summary> <pre><code class="language-java">import okhttp3.*; import com.fasterxml.jackson.databind.ObjectMapper; import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.node.ObjectNode; import java.io.File; import java.io.FileOutputStream; import java.io.IOException; import java.util.Base64; public class Main { public static void main(String[] args) throws IOException { String API_URL = "http://localhost:8080/seal-recognition"; String imagePath = "./demo.jpg"; File file = new File(imagePath); byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath()); String base64Image = Base64.getEncoder().encodeToString(fileContent); ObjectMapper objectMapper = new ObjectMapper(); ObjectNode payload = objectMapper.createObjectNode(); payload.put("file", base64Image); payload.put("fileType", 1); OkHttpClient client = new OkHttpClient(); MediaType JSON = MediaType.get("application/json; charset=utf-8"); RequestBody body = RequestBody.create(JSON, payload.toString()); Request request = new Request.Builder() .url(API_URL) .post(body) .build(); try (Response response = client.newCall(request).execute()) { if (response.isSuccessful()) { String responseBody = response.body().string(); JsonNode root = objectMapper.readTree(responseBody); JsonNode result = root.get("result"); JsonNode sealRecResults = result.get("sealRecResults"); for (int i = 0; i < sealRecResults.size(); i++) { JsonNode item = sealRecResults.get(i); int finalI = i; JsonNode prunedResult = item.get("prunedResult"); System.out.println("Pruned Result [" + i + "]: " + prunedResult.toString()); JsonNode outputImages = item.get("outputImages"); if (outputImages != null && outputImages.isObject()) { outputImages.fieldNames().forEachRemaining(imgName -> { try { String imgBase64 = outputImages.get(imgName).asText(); byte[] imgBytes = Base64.getDecoder().decode(imgBase64); String imgPath = imgName + "_" + finalI + ".jpg"; try (FileOutputStream fos = new FileOutputStream(imgPath)) { fos.write(imgBytes); System.out.println("Saved image: " + imgPath); } } catch (IOException e) { System.err.println("Failed to save image: " + e.getMessage()); } }); } } } else { System.err.println("Request failed with HTTP code: " + response.code()); } } } } </code></pre></details> <details><summary>Go</summary> <pre><code class="language-go">package main import ( "bytes" "encoding/base64" "encoding/json" "fmt" "io/ioutil" "net/http" ) func main() { API_URL := "http://localhost:8080/seal-recognition" filePath := "./demo.jpg" fileBytes, err := ioutil.ReadFile(filePath) if err != nil { fmt.Printf("Error reading file: %v\n", err) return } fileData := base64.StdEncoding.EncodeToString(fileBytes) payload := map[string]interface{}{ "file": fileData, "fileType": 1, } payloadBytes, err := json.Marshal(payload) if err != nil { fmt.Printf("Error marshaling payload: %v\n", err) return } client := &http.Client{} req, err := http.NewRequest("POST", API_URL, bytes.NewBuffer(payloadBytes)) if err != nil { fmt.Printf("Error creating request: %v\n", err) return } req.Header.Set("Content-Type", "application/json") resp, err := client.Do(req) if err != nil { fmt.Printf("Error sending request: %v\n", err) return } defer resp.Body.Close() if resp.StatusCode != http.StatusOK { fmt.Printf("Unexpected status code: %d\n", resp.StatusCode) return } body, err := ioutil.ReadAll(resp.Body) if err != nil { fmt.Printf("Error reading response body: %v\n", err) return } type SealResult struct { PrunedResult map[string]interface{} `json:"prunedResult"` OutputImages map[string]string `json:"outputImages"` InputImage *string `json:"inputImage"` } type Response struct { Result struct { SealRecResults []SealResult `json:"sealRecResults"` DataInfo interface{} `json:"dataInfo"` } `json:"result"` } var respData Response if err := json.Unmarshal(body, &respData); err != nil { fmt.Printf("Error unmarshaling response: %v\n", err) return } for i, res := range respData.Result.SealRecResults { fmt.Printf("Pruned Result %d: %+v\n", i, res.PrunedResult) for name, imgBase64 := range res.OutputImages { imgBytes, err := base64.StdEncoding.DecodeString(imgBase64) if err != nil { fmt.Printf("Error decoding image %s: %v\n", name, err) continue } filename := fmt.Sprintf("%s_%d.jpg", name, i) if err := ioutil.WriteFile(filename, imgBytes, 0644); err != nil { fmt.Printf("Error saving image %s: %v\n", filename, err) continue } fmt.Printf("Output image saved at %s\n", filename) } } } </code></pre></details> <details><summary>C#</summary> <pre><code class="language-csharp">using System; using System.IO; using System.Net.Http; using System.Text; using System.Threading.Tasks; using Newtonsoft.Json.Linq; class Program { static readonly string API_URL = "http://localhost:8080/seal-recognition"; static readonly string inputFilePath = "./demo.jpg"; static async Task Main(string[] args) { var httpClient = new HttpClient(); byte[] fileBytes = File.ReadAllBytes(inputFilePath); string fileData = Convert.ToBase64String(fileBytes); var payload = new JObject { { "file", fileData }, { "fileType", 1 } }; var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json"); HttpResponseMessage response = await httpClient.PostAsync(API_URL, content); response.EnsureSuccessStatusCode(); string responseBody = await response.Content.ReadAsStringAsync(); JObject jsonResponse = JObject.Parse(responseBody); JArray sealRecResults = (JArray)jsonResponse["result"]["sealRecResults"]; for (int i = 0; i < sealRecResults.Count; i++) { var res = sealRecResults[i]; Console.WriteLine($"[{i}] prunedResult:\n{res["prunedResult"]}"); JObject outputImages = res["outputImages"] as JObject; if (outputImages != null) { foreach (var img in outputImages) { string imgName = img.Key; string base64Img = img.Value?.ToString(); if (!string.IsNullOrEmpty(base64Img)) { string imgPath = $"{imgName}_{i}.jpg"; byte[] imageBytes = Convert.FromBase64String(base64Img); File.WriteAllBytes(imgPath, imageBytes); Console.WriteLine($"Output image saved at {imgPath}"); } } } } } } </code></pre></details> <details><summary>Node.js</summary> <pre><code class="language-js">const axios = require('axios'); const fs = require('fs'); const path = require('path'); const API_URL = 'http://localhost:8080/seal-recognition'; const imagePath = './demo.jpg'; function encodeImageToBase64(filePath) { const bitmap = fs.readFileSync(filePath); return Buffer.from(bitmap).toString('base64'); } const payload = { file: encodeImageToBase64(imagePath), fileType: 1 }; axios.post(API_URL, payload) .then((response) => { const result = response.data["result"]; const sealRecResults = result["sealRecResults"]; sealRecResults.forEach((res, i) => { console.log(`\n[${i}] prunedResult:\n`, res["prunedResult"]); const outputImages = res["outputImages"]; if (outputImages) { for (const [imgName, base64Img] of Object.entries(outputImages)) { const imgBuffer = Buffer.from(base64Img, 'base64'); const fileName = `${imgName}_${i}.jpg`; fs.writeFileSync(fileName, imgBuffer); console.log(`Output image saved at ${fileName}`); } } else { console.log(`[${i}] No outputImages found.`); } }); }) .catch((error) => { console.error('Error occurred while calling the API:', error.message); }); </code></pre></details> <details><summary>PHP</summary> <pre><code class="language-php"><?php $API_URL = "http://localhost:8080/seal-recognition"; $image_path = "./demo.jpg"; $image_data = base64_encode(file_get_contents($image_path)); $payload = array("file" => $image_data, "fileType" => 1); $ch = curl_init($API_URL); curl_setopt($ch, CURLOPT_POST, true); curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload)); curl_setopt($ch, CURLOPT_HTTPHEADER, array('Content-Type: application/json')); curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); $response = curl_exec($ch); curl_close($ch); $result = json_decode($response, true)["result"]["sealRecResults"]; foreach ($result as $i => $item) { echo "[$i] prunedResult:\n"; print_r($item["prunedResult"]); if (!empty($item["outputImages"])) { foreach ($item["outputImages"] as $img_name => $base64_img) { if (!empty($base64_img)) { $output_path = "{$img_name}_{$i}.jpg"; file_put_contents($output_path, base64_decode($base64_img)); echo "Output image saved at $output_path\n"; } } } else { echo "No outputImages found for item $i\n"; } } ?> </code></pre></details> </details>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 <b>fine-tune</b> the existing models using <b>your own domain-specific or application data</b> to improve the recognition performance of the seal text recognition pipeline in your scenario.
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.
<table> <thead> <tr> <th>Scenario</th> <th>Fine-Tuning Module</th> <th>Fine-Tuning Reference Link</th> </tr> </thead> <tbody> <tr> <td>Inaccurate or missing seal position detection</td> <td>Layout Detection Module</td> <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html#iv-custom-development">Link</a></td> </tr> <tr> <td>Missing text detection</td> <td>Text Detection Module</td> <td><a href="https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/module_usage/text_detection.html#4-custom-development">Link</a></td> </tr> <tr> <td>Inaccurate text content</td> <td>Text Recognition Module</td> <td><a href="https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/module_usage/text_recognition.html#41-dataset-and-pre-trained-model-preparation">Link</a></td> </tr> <tr> <td>Inaccurate full-image rotation correction</td> <td>Document Image Orientation Classification Module</td> <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html#iv-custom-development">Link</a></td> </tr> <tr> <td>Inaccurate image distortion correction</td> <td>Text Image Correction Module</td> <td>Not supported for fine-tuning</td> </tr> </tbody> </table>After you complete the fine-tuning training with a private dataset, you can obtain the local model weight files. You can then use the fine-tuned model weights by specifying the local model save path through parameters or by using a custom pipeline configuration file.
When initializing the pipeline object, specify the local model path through parameters. Taking the usage of fine-tuned weights for a text detection model as an example, the demonstration is as follows:
Command line method:
# Specify the local model path through --doc_orientation_classify_model_dir
paddleocr seal_recognition -i ./seal_text_det.png --doc_orientation_classify_model_dir your_orientation_classify_model_path
# By default, the PP-LCNet_x1_0_doc_ori model is used as the default text detection model. If the fine-tuned model is not this one, modify the model name with --text_detection_model_name
paddleocr seal_recognition -i ./seal_text_det.png --doc_orientation_classify_model_name PP-LCNet_x1_0_doc_ori --doc_orientation_classify_model_dir your_orientation_classify_model_path
Script method:
from paddleocr import SealRecognition
# Specify the local model path through doc_orientation_classify_model_dir
pipeline = SealRecognition(doc_orientation_classify_model_dir ="./your_orientation_classify_model_path")
# By default, the PP-LCNet_x1_0_doc_ori model is used as the default text detection model. If the fine-tuned model is not this one, modify the model name with doc_orientation_classify_model_name
# pipeline = SealRecognition(doc_orientation_classify_model_name="PP-LCNet_x1_0_doc_ori", doc_orientation_classify_model_dir="./your_orientation_classify_model_path")
You can call the export_paddlex_config_to_yaml method of the general OCR pipeline object in PaddleOCR to export the current pipeline configuration to a YAML file:
from paddleocr import SealRecognition
pipeline = SealRecognition()
pipeline.export_paddlex_config_to_yaml("SealRecognition.yaml")
After obtaining the default pipeline configuration file, replace the local path of the fine-tuned model weights in the corresponding position of the pipeline configuration file. For example:
......
SubPipelines:
DocPreprocessor:
SubModules:
DocOrientationClassify:
model_dir: null # Replace with the path of the fine-tuned document orientation classification model weights
model_name: PP-LCNet_x1_0_doc_ori # If the name of the fine-tuned model is different from the default model name, please also modify here
module_name: doc_text_orientation
DocUnwarping:
model_dir: null # Replace with the path of the fine-tuned document unwarping model weights
model_name: UVDoc # If the name of the fine-tuned model is different from the default model name, please also modify here
module_name: image_unwarping
pipeline_name: doc_preprocessor
use_doc_orientation_classify: true
use_doc_unwarping: true
......
The pipeline configuration file not only contains the parameters supported by the SealRecognition CLI and Python API but also allows for more advanced configurations. Detailed information can be found in the PaddleX Model pipeline Usage Overview, where you can find the corresponding pipeline usage tutorial and adjust various configurations as needed.
After modifying the configuration file, specify the path of the modified pipeline configuration file using the --paddlex_config parameter in the command line. PaddleOCR will read its contents as the pipeline configuration. Example:
paddleocr seal_recognition --paddlex_config SealRecognition.yaml ...
When initializing the pipeline object, you can pass the PaddleX pipeline configuration file path or configuration dictionary through the paddlex_config parameter. PaddleOCR will read its contents as the pipeline configuration. Example:
from paddleocr import SealRecognition
pipeline = SealRecognition(paddlex_config="SealRecognition.yaml")