MFE-GAN: Efficient GAN-based Framework for Document Image Enhancement and Binarization with Multi-scale Feature Extraction

TL;DR

MFE-GAN introduces a multi-scale feature extraction framework for document image enhancement and binarization, significantly reducing training and inference times while maintaining high performance in OCR tasks.

Contribution

The paper proposes a novel efficient GAN framework with multi-scale feature extraction, incorporating Haar wavelet transformation and new network components, to improve speed without sacrificing accuracy.

Findings

Reduces training and inference times compared to state-of-the-art methods

Maintains comparable enhancement and binarization performance

Effective in diverse datasets like Benchmark, Nabuco, and CMATERdb

Abstract

Document image enhancement and binarization are commonly performed prior to document analysis and recognition tasks for improving the efficiency and accuracy of optical character recognition (OCR) systems. This is because directly recognizing text in degraded documents, particularly in color images, often results in unsatisfactory recognition performance. To address these issues, existing methods train independent generative adversarial networks (GANs) for different color channels to remove shadows and noise, which, in turn, facilitates efficient text information extraction. However, deploying multiple GANs results in long training and inference times. To reduce both training and inference times of document image enhancement and binarization models, we propose MFE-GAN, an efficient GAN-based framework with multi-scale feature extraction (MFE), which incorporates Haar wavelet transformation (HWT) and normalization to process document images before feeding them into GANs for training. In addition, we present novel generators, discriminators, and loss functions to improve the model's performance, and we conduct ablation studies to demonstrate their effectiveness. Experimental results on the Benchmark, Nabuco, and CMATERdb datasets demonstrate that the proposed MFE-GAN significantly reduces the total training and inference times while maintaining comparable performance with respect to state-of-the-art (SOTA) methods. The implementation of this work is available at https://ruiyangju.github.io/MFE-GAN.