Text Image Unwarping Module Development Tutorial¶

I. Overview¶

The primary purpose of Text Image Unwarping is to perform geometric transformations on images in order to correct issues such as document distortion, tilt, perspective deformation, etc., enabling more accurate recognition by subsequent text recognition modules.

II. Supported Model List¶

Model Name	Model Download Link	MS-SSIM （%）	Model Size (M)	information
UVDoc	Inference Model/Trained Model	54.40	30.3 M	High-precision Text Image Unwarping 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 Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
Inference Mode Description

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Normal Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Optimal combination of pre-selected precision types and acceleration strategies	FP32 Precision / 8 Threads	Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.)

III. Quick Integration¶

❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the PaddleX Local Installation Guide

Just a few lines of code can complete the inference of the Text Image Unwarping module, allowing you to easily switch between models under this module. You can also integrate the model inference of the the Text Image Unwarping module into your project.

Before running the following code, please download the demo image to your local machine.

from paddlex import create_model
model = create_model(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', 'doctr_img': '...'}"}

The meanings of the running result parameters are as follows: - input_path: Indicates the path of the input image to be corrected. - doctr_img: Indicates the result of the corrected image. Since there is too much data to print directly, ... is used here as a placeholder. The prediction result can be saved as an image through res.save_to_img() and as a JSON file through res.save_to_json().

The visualization image is as follows:

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Relevant methods, parameters, and explanations are as follows:

create_model instantiates an image correction model (here using UVDoc as an example). The specific explanation is as follows:

Parameter	Parameter Description	Parameter Type	Options	Default Value
`model_name`	Name of the model	`str`	All model names supported by PaddleX	None
`model_dir`	Path to store the model	`str`	None	None

The model_name must be specified. After specifying model_name, the default model parameters built into PaddleX will be used. If model_dir is specified, the user-defined model will be used.
The predict() method of the image correction model is called for inference prediction. The parameters of the predict() method are input and batch_size, with specific explanations as follows:

Parameter	Parameter Description	Parameter Type	Options	Default Value
`input`	Data to be predicted, supporting multiple input types	`Python Var`/`str`/`dict`/`list`	Python variable, such as image data represented by `numpy.ndarray` 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 List, the elements of the list should be of the above-mentioned data 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

The prediction results are processed, with each sample's prediction result being of type dict, and supporting 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 non-`ASCII` characters are escaped 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 JSON file	`save_path`	`str`	The file path for saving. When it is a directory, the saved file name will match 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 non-`ASCII` characters are escaped 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 an image file	`save_path`	`str`	The file path for saving. When it is a directory, the saved file name will match the input file name	None

Additionally, it also supports obtaining visualized images with results and prediction results through attributes, as follows:

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

For more information on using PaddleX's single-model inference API, refer to the PaddleX Single Model Python Script Usage Instructions.

IV. Custom Development¶

The current module temporarily does not support fine-tuning training and only supports inference integration. Fine-tuning training for this module is planned to be supported in the future.

You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the PaddleX High-Performance Inference Guide.

Text Image Unwarping Module Development Tutorial¶

I. Overview¶

II. Supported Model List¶

III. Quick Integration¶

IV. Custom Development¶

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