Document Binarization via Multi-resolutional Attention Model with DRD Loss

Abstract

Document binarization which separates text from background is a critical pre-processing step for many high level document analysis tasks. Conventional document binarization approaches tend to use hand-craft features and empirical rules to simulate the degradation process of document image and accomplish the binarization task. In this paper, we propose a deep learning framework where the probability of text areas is inferred through a multi-resolutional attention model, which is consequently fed into a convolutional conditional random field (ConvCRF) to obtain the final binarized document image. In the proposed approach, the features of degraded document image are learned by neural networks and the relations between text areas and backgrounds are inferred by ConvCRF, which avoids the dependence of domain knowledge from researchers and has more generalization capabilities. The experimental results on public datasets show that the proposed method has superior binarization performance than the existing state-of-the-art approaches.