A novel approach to text binarization via a diffusion-based model

This paper presents a new approach to document image binarization. The method is based on the dynamic process of diffusion, coupled with a nonlinear Fitzhugh-Nagumo type source term that exhibits binarizing properties. These desirable properties lead to a method that is robust to noise and is able to successfully binarize an input document image. We measure the efficacy of our proposed method against industry standards by two methods, a pixel by pixel comparison with the ground truth image and a standard optical character recognition test. Through these measures we illustrate a progressive method that performs at the highest level in the field.