Evaluating Deep Neural Networks for Image Document Enhancement

TL;DR

This paper evaluates six deep neural network architectures for enhancing camera-captured document images, demonstrating their effectiveness and establishing a baseline for future deep learning research in document image enhancement.

Contribution

It introduces a comprehensive evaluation of multiple DNN architectures for document enhancement and proposes an IQA-based methodology for quantitative comparison.

Findings

DNNs outperform traditional methods in image enhancement quality

Certain architectures achieve significant improvements in image clarity

The evaluation framework facilitates future research in this domain

Abstract

This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.