Learning 2D Morphological Network for Old Document Image Binarization Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through backpropagation. The proposed network has been evaluated on DIBCO 2017 and HDIBCO 2018 and ISILetter dataset. Our results show more convincing as compared to the results of other stateoftheart methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https:github.comranjanZICDAR_Binarization

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Images using Convolutional Neural Networks Based on Predicted TwoChannel Images Due to the poor condition of most of historical documents, binarization is difficult to separate document image background pixels from foreground pixels.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