UVDoc

Introduction

The main purpose of text image correction is to carry out geometric transformation on the image to correct the document distortion, inclination, perspective deformation and other problems in the image, so that the subsequent text recognition can be more accurate.

Model	CER
UVDoc	0.179

Note: Test data set: docunet benchmark data set.

Quick Start

Installation

1. PaddlePaddle

Please refer to the following commands to install PaddlePaddle using pip:

# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.

2. PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

paddleocr text_image_unwarping --model_name UVDoc -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/SfMVKd0xnMII5KBDV6Mfz.jpeg

You can also integrate the model inference of the TextImageUnwarping module into your project. Before running the following code, please download the sample image to your local machine.

from paddleocr import TextImageUnwarping

model = TextImageUnwarping(model_name="UVDoc")
output = model.predict("SfMVKd0xnMII5KBDV6Mfz.jpeg", 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 obtained result is as follows:

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

The visualized image is as follows: