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Text Image Rectification Module Usage Tutorial

1. Overview

The primary purpose of text image rectification is to perform geometric transformations on images to correct distortions, inclinations, perspective deformations, etc., in the document images for more accurate subsequent text recognition.

2. Supported Model List

ModelModel Download Link CER Model Storage Size (M) Description
UVDocInference Model/Training Model 0.179 30.3 M High-accuracy text image rectification model

Test Environment Description:

  • Performance Test Environment
    • Test Dataset: DocUNet benchmark dataset.
    • Hardware Configuration:
      • GPU: NVIDIA Tesla T4
      • CPU: Intel Xeon Gold 6271C @ 2.60GHz
      • Other Environment: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
  • Inference Mode Explanation
Mode GPU Configuration CPU Configuration Acceleration Technology Combination
Regular Mode FP32 Precision / No TRT Acceleration FP32 Precision / 8 Threads PaddleInference
High-Performance Mode Choose the optimal combination of prior precision type and acceleration strategy FP32 Precision / 8 Threads Choose the optimal prior backend (Paddle/OpenVINO/TRT, etc.)

3. Quick Start

❗ Before starting quickly, please first install the PaddleOCR wheel package. For details, please refer to the installation tutorial.

You can quickly experience it with one command:

paddleocr text_image_unwarping -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg

You can also integrate the model inference from the image rectification module into your project. Before running the following code, please download the sample image locally.

from paddleocr import TextImageUnwarping
model = TextImageUnwarping(model_name="UVDoc")
output = model.predict("doc_test.jpg", 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 obtained is:

{'res': {'input_path': 'doc_test.jpg', 'page_index': None, 'doctr_img': '...'}}

The meanings of the parameters in the result are as follows: - input_path: Indicates the path of the image to be rectified - doctr_img: Indicates the rectified image result. Due to the large amount of data, it is not convenient to print directly, so it is replaced here with .... You can use res.save_to_img() to save the prediction result as an image, and res.save_to_json() to save the prediction result as a json file.

The visualized image is as follows:

The relevant methods, parameters, etc., are described as follows:

  • TextImageUnwarping instantiates the image rectification model (taking UVDoc as an example here), with specific explanations as follows:
Parameter Description Type Options Default Value
model_name Model Name str All model names supported by PaddleX None
model_dir Model Storage Path str None None
device Model Inference Device str Supports specifying specific GPU card numbers, such as “gpu:0”, specific hardware card numbers, such as “npu:0”, CPU as “cpu”. gpu:0
use_hpip Whether to enable high-performance inference plugin bool None False
hpi_config High-Performance Inference Configuration dict | None None None

  • Among them, model_name must be specified. After specifying model_name, the default model parameters built into PaddleX are used. When model_dir is specified, the user-defined model is used.

  • Call the predict() method of the image rectification model for inference prediction. This method will return a result list. Additionally, this module also provides a predict_iter() method. Both methods are consistent in terms of parameter acceptance and result return. The difference is that predict_iter() returns a generator, which can process and obtain prediction results step by step, suitable for handling large datasets or scenarios where memory saving is desired. You can choose to use either of these methods according to your actual needs. The predict() method has parameters input and batch_size, with specific explanations as follows:

Parameter Description Type Options Default Value
input Data to be predicted, supports multiple input types Python Var/str/dict/list
  • Python Variable, such as numpy.ndarray representing image data
  • File Path, such as the local path of an image file: /root/data/img.jpg
  • URL Link, such as the network URL of an image file: Example
  • Local Directory, which should contain data files to be predicted, such as the local path: /root/data/
  • List, where list elements must be of the above types, such as [numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"]
 None
batch_size Batch Size int Any integer 1
  • Process the prediction results. The prediction result for each sample is a corresponding Result object, which supports printing, saving as an image, and saving as a json file:
Method Description Parameter Type Parameter Description Default Value
print() Print result to terminal format_json bool Whether to format the output content using JSON indentation True
indent int Specifies the indentation level to beautify the output JSON data, making it more readable, effective only when format_json is True 4
ensure_ascii bool Controls whether to escape non-ASCII characters into 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 the result as a json format file save_path str The path to save the file. When specified as a directory, the saved file is named consistent with the input file type. None
indent int Specifies the indentation level to beautify the output JSON data, making it more readable, effective only when format_json is True 4
ensure_ascii bool Controls whether to escape non-ASCII characters into 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 the result as an image format file save_path str The path to save the file. When specified as a directory, the saved file is named consistent with the input file type. None
  • Additionally, the result can be obtained through attributes that provide the visualized images with results and the prediction results, as follows:
Attribute Description
json Get the prediction result in json format
img Get the visualized image in dict format

4. Secondary Development

The current module does not support fine-tuning training and only supports inference integration. Concerning fine-tuning training for this module, there are plans to support it in the future.

5. FAQ

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