Discrete CRF Based Combination Framework for Document Image Binarization

Document image binarization is still an active research area as shows the number of binarization techniques proposed since many decades. The binarization of degraded document images is still difficult and encourages the development of new algorithms. For the last decade, discrete conditional random fields have been successfully used for many domains such as automatic language analysis. In this paper, we propose a CRF based framework to explore the combination capabilities of this model by combining discrete outputs from several well known binarization algorithms. The framework uses two 1D CRF models on the horizontal and the vertical directions that are coupled for each pixel by the product of the marginal probabilities computed from the both models. Experiments are made on two datasets from the Document Image Binarization Contest (DIBCO) 2009 and 2011 and show best performances than most of the methods presented at DIBCO 2011.