Evaluating Deep Neural Networks for Image Document Enhancement
It addresses the problem of improving document image quality for users in fields like digitization, but it is incremental as it primarily benchmarks existing methods.
This paper evaluated six deep neural network architectures for enhancing camera-captured document images, finding that the best ones outperformed a traditional computer vision approach and could serve as a baseline for future work.
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.