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Images using Convolutional Neural Networks Based on Predicted TwoChannel Images Due to the poor condition of most of historical documents, binarization is difficult to separate document image background pixels from foreground pixels.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.

CNN Based Binarization of MultiSpectral Document Images This work is concerned with the binarization of ancient manuscripts that have been imaged with a MultiSpectral Imaging (MSI) system. We introduce a new dataset for this purpose that is composed of 130 multispectral images taken from two medieval manuscripts. We propose to apply an endtoend Convolutional Neural Network (CNN) for the segmentation of the historical writings. The performance of the CNN based method is superior compared to two stateoftheart methods that are especially designed for multispectral document images. The CNN based method is also evaluated on a previous and smaller database, where its performance is slightly worse than the two stateoftheart techniques.