AIStoryBoard/python/PaddleOCR-main/docs/version3.x/module_usage/seal_text_detection.en.md

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Seal Text Detection Module Tutorial

I. Overview

The seal text detection module typically outputs multi-point bounding boxes around text regions, which are then passed as inputs to the distortion correction and text recognition modules for subsequent processing to identify the textual content of the seal. Recognizing seal text is an integral part of document processing and finds applications in various scenarios such as contract comparison, inventory access auditing, and invoice reimbursement verification. The seal text detection module serves as a subtask within OCR (Optical Character Recognition), responsible for locating and marking the regions containing seal text within an image. The performance of this module directly impacts the accuracy and efficiency of the entire seal text OCR system.

II. Supported Model List

The inference time only includes the model inference time and does not include the time for pre- or post-processing.

Model Name	Model Download Link	Hmean（%）	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PP-OCRv4_server_seal_det	Inference Model/Training Model	98.40	124.64 / 91.57	545.68 / 439.86	109	The server-side seal text detection model of PP-OCRv4 boasts higher accuracy and is suitable for deployment on better-equipped servers.
PP-OCRv4_mobile_seal_det	Inference Model/Training Model	96.36	9.70 / 3.56	50.38 / 19.64	4.6	The mobile-side seal text detection model of PP-OCRv4, on the other hand, offers greater efficiency and is suitable for deployment on end devices.

Test Environment Description:

  <li><b>Performance Test Environment</b>
      <ul>
           <li><strong>Test Dataset：</strong> A Self-built Internal Dataset, Containing 500 Images of Circular Stamps.</li>
          <li><strong>Hardware Configuration:</strong>
              <ul>
                  <li>GPU: NVIDIA Tesla T4</li>
                  <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
              </ul>
          </li>
          <li><strong>Software Environment:</strong>
              <ul>
                  <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                  <li>paddlepaddle 3.0.0 / paddleocr 3.0.3</li>
              </ul>
          </li>
      </ul>
  </li>
  <li><b>Inference Mode Description</b></li>

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.)

III. Quick Integration

❗ Before quick integration, please install the PaddleOCR wheel package. For detailed instructions, refer to PaddleOCR Local Installation Tutorial。

Quickly experience with just one command:

paddleocr seal_text_detection -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png

Note: The official models would be download from HuggingFace by default. If can't access to HuggingFace, please set the environment variable PADDLE_PDX_MODEL_SOURCE="BOS" to change the model source to BOS. In the future, more model sources will be supported.

You can also integrate the model inference from the layout area detection module into your project. Before running the following code, please download Example Image Go to the local area.

from paddleocr import SealTextDetection
model = SealTextDetection(model_name="PP-OCRv4_server_seal_det")
output = model.predict("seal_text_det.png", batch_size=1)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

After running, the result is:

{'res': {'input_path': 'seal_text_det.png', 'page_index': None, 'dt_polys': [array([[463, 477],
       ...,
       [428, 505]]), array([[297, 444],
       ...,
       [230, 443]]), array([[457, 346],
       ...,
       [267, 345]]), array([[325,  38],
       ...,
       [322,  37]])], 'dt_scores': [0.9912680344777314, 0.9906849624837963, 0.9847219455533163, 0.9914791724153904]}}

The meanings of the parameters are as follows:

• input_path：represents the path of the input image to be predicted
• dt_polys：represents the predicted text detection boxes, where each text detection box contains multiple vertices of a polygon. Each vertex is a list of two elements, representing the x and y coordinates of the vertex respectively
• dt_scores：represents the confidence scores of the predicted text detection boxes

The visualization image is as follows:

The explanations of related methods and parameters are as follows:

• SealTextDetection instantiates a text detection model (here we take PP-OCRv4_server_seal_det as an example), and the specific explanations are as follows:

  Parameter	Description	Type	Default
  model_name	Meaning:Model name. Description: If set to None, PP-OCRv4_mobile_seal_det will be used.	str|None	None
  model_dir	Meaning:Model storage path.	str|None	None
  device	Meaning:Device for inference. Description: For example: "cpu", "gpu", "npu", "gpu:0", "gpu:0,1". If multiple devices are specified, parallel inference will be performed. By default, GPU 0 is used if available; otherwise, CPU is used.	str|None	None
  enable_hpi	Meaning:Whether to enable high-performance inference.	bool	False
  use_tensorrt	Meaning:Whether to use the Paddle Inference TensorRT subgraph engine. Description: If the model does not support acceleration through TensorRT, setting this flag will not enable acceleration. For Paddle with CUDA version 11.8, the compatible TensorRT version is 8.x (x>=6), and it is recommended to install TensorRT 8.6.1.6.
  bool	False
  precision	Meaning:Computation precision when using the TensorRT subgraph engine in Paddle Inference. Description: Options: "fp32", "fp16".	str	"fp32"
  enable_mkldnn	Meaning:Whether to enable MKL-DNN acceleration for inference. Description: If MKL-DNN is unavailable or the model does not support it, acceleration will not be used even if this flag is set.	bool	True
  mkldnn_cache_capacity	Meaning:MKL-DNN cache capacity.	int	10
  cpu_threads	Meaning:Number of threads to use for inference on CPUs.	int	10
  limit_side_len	Meaning:Limit on the side length of the input image for detection. Description: int specifies the value. If set to None, the model's default configuration will be used.	int|None	None
  limit_type	Meaning:Type of image side length limitation. Description: "min" ensures the shortest side of the image is no less than det_limit_side_len; "max" ensures the longest side is no greater than limit_side_len. If set to None, the model's default configuration will be used.	str|None	None
  thresh	Meaning:Pixel score threshold. Description: Pixels in the output probability map with scores greater than this threshold are considered text pixels. Accepts any float value greater than 0. If set to None, the model's default configuration will be used.	float|None	None
  box_thresh	Meaning:If the average score of all pixels inside the bounding box is greater than this threshold, the result is considered a text region. Description: Accepts any float value greater than 0. If set to None, the model's default configuration will be used.	float|None	None
  unclip_ratio	Meaning:Expansion ratio for the Vatti clipping algorithm, used to expand the text region. Description:Accepts any float value greater than 0. If set to None, the model's default configuration will be used.	float|None	None
  input_shape	Meaning:Input image size for the model in the format (C, H, W). Description: If set to None, the model's default size will be used.	tuple|None	None

• The predict() method of the seal text detection model is called for inference prediction. The parameters of the predict() method include input 、batch_size、 limit_side_len、 limit_type、 thresh、 box_thresh、 max_candidates、unclip_ratio. The specific descriptions are as follows:

  Parameter	Description	Type	Default
  input	Meaning:Input data to be predicted. Required. Description: Supports multiple input types: Python Var: e.g., numpy.ndarray representing image data str: Local image or PDF file path: /root/data/img.jpg; URL of image or PDF file: e.g., example; Local directory: directory containing images for prediction, e.g., /root/data/ (Note: directories containing PDF files are not supported; PDFs must be specified by exact file path) list: Elements must be of the above types, e.g., [numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"]	Python Var|str|list
  batch_size	Meaning:Batch size. Description: Can be set to any positive integer.	int	1
  limit_side_len	Meaning:Same meaning as the instantiation parameters. Description: If set to None, the instantiation value is used; otherwise, this parameter takes precedence.	int|None	None
  limit_type	Meaning:Same meaning as the instantiation parameters. Description: If set to None, the instantiation value is used; otherwise, this parameter takes precedence.	str|None	None
  thresh	Meaning:Same meaning as the instantiation parameters. Description: If set to None, the instantiation value is used; otherwise, this parameter takes precedence.	float|None	None
  box_thresh	Meaning:Same meaning as the instantiation parameters. Description: If set to None, the instantiation value is used; otherwise, this parameter takes precedence.	float|None	None
  unclip_ratio	Meaning:Same meaning as the instantiation parameters. Description: If set to None, the instantiation value is used; otherwise, this parameter takes precedence.	float|None	None

Process the prediction results. Each sample's prediction result is a corresponding Result object, and it supports operations such as printing, saving as an image, and saving as a json file:

Method	Method Description	Parameter	Parameter Type	Parameter Description	Default Value
print()	Print the result to the terminal	format_json	bool	Whether to format the output content using JSON indentation	True
		indent	int	Specify the indentation level to beautify the output JSON data, making it more readable. This is only effective when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. This is only effective when format_json is True	False
save_to_json()	Save the result as a file in JSON format	save_path	str	The file path for saving. When it is a directory, the saved file name will be consistent with the input file name	None
		indent	int	Specify the indentation level to beautify the output JSON data, making it more readable. This is only effective when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. This is only effective when format_json is True	False
save_to_img()	Save the result as a file in image format	save_path	str	The file path for saving. When it is a directory, the saved file name will be consistent with the input file name	None

• In addition, it also supports obtaining visual images with results and prediction results through attributes, as follows:

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

IV. Custom Development

If the above model is still not performing well in your scenario, you can try the following steps for secondary development. Here, we'll use training PP-OCRv4_server_seal_det as an example; you can replace it with the corresponding configuration files for other models. First, you need to prepare a text detection dataset. You can refer to the format of the seal text detection demo data for preparation. Once prepared, you can follow the steps below for model training and export. After export, you can quickly integrate the model into the above API. This example uses a seal text detection demo dataset. Before training the model, please ensure that you have installed the dependencies required by PaddleOCR as per the installation documentation.

4.1 Dataset and Pre-trained Model Preparation

4.1.1 Preparing the Dataset

wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ocr_curve_det_dataset_examples.tar -P ./dataset
tar -xf ./dataset/ocr_curve_det_dataset_examples.tar -C ./dataset/

4.1.1 Preparing the pre-trained model

wget https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_seal_det_pretrained.pdparams

4.2 Model Training

PaddleOCR has modularized the code, and when training the PP-OCRv4_server_seal_det model, you need to use the configuration file for PP-OCRv4_server_seal_det.

The training commands are as follows:

# Single GPU training (default training method)
python3 tools/train.py -c configs/det/PP-OCRv4/PP-OCRv4_server_seal_det.yml \
   -o Global.pretrained_model=./PP-OCRv4_server_seal_det_pretrained.pdparams \
   Train.dataset.data_dir=./dataset/ocr_curve_det_dataset_examples Train.dataset.label_file_list=./dataset/ocr_curve_det_dataset_examples/train.txt \
   Eval.dataset.data_dir=./dataset/ocr_curve_det_dataset_examples Eval.dataset.label_file_list=./dataset/ocr_curve_det_dataset_examples/val.txt
   
# Multi-GPU training, specify GPU ids using the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/det/PP-OCRv4/PP-OCRv4_server_seal_det.yml \
   -o Global.pretrained_model=./PP-OCRv4_server_seal_det_pretrained.pdparams \
   Train.dataset.data_dir=./dataset/ocr_curve_det_dataset_examples Train.dataset.label_file_list=./dataset/ocr_curve_det_dataset_examples/train.txt \
   Eval.dataset.data_dir=./dataset/ocr_curve_det_dataset_examples Eval.dataset.label_file_list=./dataset/ocr_curve_det_dataset_examples/val.txt

4.3 Model Evaluation

You can evaluate the trained weights, such as output/xxx/xxx.pdparams, using the following command:

# Make sure to set the pretrained_model path to the local path. If using a model that was trained and saved by yourself, be sure to modify the path and filename to {path/to/weights}/{model_name}.
# Demo test set evaluation
python3 tools/eval.py -c configs/det/PP-OCRv4/PP-OCRv4_server_seal_det.yml -o \
    Global.pretrained_model=output/xxx/xxx.pdparams

4.4 Model Export

python3 tools/export_model.py -c configs/det/PP-OCRv4/PP-OCRv4_server_seal_det.yml -o \
    Global.pretrained_model=output/xxx/xxx.pdparams \
    Global.save_inference_dir="./PP-OCRv4_server_seal_det_infer/"

After exporting the model, the static graph model will be stored in the ./PP-OCRv4_server_seal_det_infer/ directory. In this directory, you will see the following files:

./PP-OCRv4_server_seal_det_infer/
├── inference.json
├── inference.pdiparams
├── inference.yml

With this, the secondary development is complete, and the static graph model can be directly integrated into PaddleOCR's API.

5. FAQ