A novel variational model for noise robust document image binarization

Abstract

Abstract Binarization is a fundamental problem in document image analysis systems. Different from current thresholding techniques, a novel variational model is proposed for noise robust document image binarization in this work, which is inspired by two classical variational models that have been successfully applied in image segmentation and denoising. In our variational model, the energy functional consists of three terms: data fidelity term, binary classification term and regularization term. As a result, the proposed model is capable of performing image binarization and suppressing noise simultaneously. Concretely, we firstly design a variational model for the original document image, the minimizer of which is our desired binarization result. Secondly, the gradient descent flow equation of our model is derived by means of variational principle. Lastly, a simple finite difference scheme, time forward and space center difference, is employed to solve the gradient descent flow equation. Extensive experiments are conducted on three types of document image datasets to validate our model qualitatively and quantitatively. The experiment results not only demonstrate the effectiveness of our approach, but also verify its noise and illumination robustness.