Document Binarization via Multiresolutional Attention Model with DRD Loss Document binarization which separates text from background is a critical preprocessing step for many high level document analysis tasks. Conventional document binarization approaches tend to use handcraft 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 multiresolutional 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 stateoftheart approaches.

Binarization of Degraded Document Images using Convolutional Neural Networks Based on Predicted TwoChannel Images Due to the poor condition of most of historical documents, binarization is difficult to separate document image background pixels from foreground pixels. This paper proposes Convolutional Neural Networks (CNNs) based on predicted twochannel images in which CNNs are trained to classify the foreground pixels. The promising results from the use of multispectral images for semantic segmentation inspired our efforts to create a novel predictionbased twochannel image. In our method, the original image is binarized by the structural symmetric pixels (SSPs) method, and the twochannel image is constructed from the original image and its binarized image. In order to explore impact of proposed twochannel images as network inputs, we use two popular CNNs architectures, namely SegNet and Unet. The results presented in this work show that our approach fully outperforms SegNet and Unet when trained by the original images and demonstrates competitiveness and robustness compared with stateoftheart results using the DIBCO database.