A Dilated MultiRes Visual Attention U-Net for historical document image binarization

Abstract

The task of binarization of historical document images has been in the forefront of image processing research, during the digital transition of libraries. The process of storing and transcribing valuable historical printed or handwritten material can salvage world cultural heritage and make it available online without physical attendance. The task of binarization can be viewed as a pre-processing step that attempts to separate the printed/handwritten characters in the image from possible noise and stains, which will assist in the Optical Character Recognition (OCR) process. Many approaches have been proposed before, including deep learning based approaches. In this article, we propose a U-Net style deep learning architecture that incorporates many other developments of deep learning, including residual connections, multi-resolution connections, visual attention blocks and dilated convolution blocks for upsampling. The novelties in the proposed DMVAnet lie in the use of these elements in combination in a novel U-Net style architecture and the application of DMVAnet in image binarization for the first time. In addition, the proposed DMVAnet is a very computationally lightweight network that performs very close or even better than the state-of-the-art approaches with a fraction of the network size and parameters. Finally, it can be used on platforms with restricted processing power and system resources, such as mobile devices and through scaling can result in inference times that allow for real-time applications.