Document image binarization using a discriminative structural classifier

Binarization is one of the key initial steps in image analysis and system understanding. Different types of document degradations make the binarization a very challenging task. This paper proposes a statistical framework for binarizing degraded document images based on the concept of conditional random fields (CRFs). The CRFs are discriminative graphical models which model conditional distribution and are used in structural classifications. The distribution of binarized images given the degraded ones is modelled with respect to a set of informative features extracted for all sites of the document image. The recent marginal based learning method [5] is used for the estimation of parameters of the model. The proposed graphical framework enables the depending labelling of all the sites of image despite the independent pixel-by-pixel binarization of other methods. The performance of our system is evaluated on different document image datasets and is compared with several well-known binarization methods. Experimental results show comparable performance with respect to other state-of-the-art methods.