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¶

Model	Model Download Link	CER	Model Storage Size (M)	Description
UVDoc	Inference 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	Default
`model_name`	Name of the model	`str`	`None`
`model_dir`	Model storage path	`str`	`None`
`device`	Device(s) to use for inference. Examples: `cpu`, `gpu`, `npu`, `gpu:0`, `gpu:0,1`. If multiple devices are specified, inference will be performed in parallel. Note that parallel inference is not always supported. By default, GPU 0 will be used if available; otherwise, the CPU will be used.	`str`	`None`
`enable_hpi`	Whether to use the high performance inference.	`bool`	`False`
`use_tensorrt`	Whether to use the Paddle Inference TensorRT subgraph engine.	`bool`	`False`
`min_subgraph_size`	Minimum subgraph size for TensorRT when using the Paddle Inference TensorRT subgraph engine.	`int`	`3`
`precision`	Precision for TensorRT when using the Paddle Inference TensorRT subgraph engine. Options: `fp32`, `fp16`, etc.	`str`	`fp32`
`enable_mkldnn`	Whether to use acceleration for inference.	`bool`	`True`
`cpu_threads`	Number of threads to use for inference on CPUs.	`int`	`10`

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	Default
`input`	Input data to be predicted. Required. 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`	Batch size, positive integer.	`int`	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.

Text Image Rectification Module Usage Tutorial¶

1. Overview¶

2. Supported Model List¶

3. Quick Start¶

4. Secondary Development¶

5. FAQ¶

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