PaddleX/docs/module_usage/tutorials/ocr_modules/text_image_unwarping.en.md

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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

The accuracy metrics of the above models are measured on the [DocUNet benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) dataset. ## III. Quick Integration > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md) 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](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg) to your local machine. ```python 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: ```bash {'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: Note: Due to network issues, the above URL may not be successfully parsed. If you need the content of this webpage, please check the validity of the link and try again. Alternatively, if parsing this link is not necessary for your question, please proceed with other questions. 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 Local directory, the directory should contain data files to be predicted, such as the local path: /root/data/ Dictionary, the key of the dictionary must correspond to the specific task, such as "img" for image classification tasks. The value of the dictionary supports the above types of data, for example: {"img": "/root/data1"} List, elements of the list must be the above types of data, such as [numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"], [{"img": "/root/data1"}, {"img": "/root/data2/img.jpg"}]	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.