In this paper, we present a novel approach for super-resolved binarization of document images acquired by low quality devices. The algorithm tries to compute the super resolution of the likelihood of text instead of the gray value of pixels. This method is the extension of a binarization algorithm (FAIR: a Fast Algorithm for document Image Restoration) which has been submitted into different contests where it showed good performances. The method can be considered as parameter free and is based on a rough localization of text in order to save computation time. Experimental results on several image sequences presenting a background noise and variation in contrast and illumination show the effectiveness of the method.

This paper presents a new approach for the binarization of seriously degraded manuscript. We introduce a new technique based on a Markov random field (MRF) model of the document. Depending on the available information, the model parameters (clique potentials) are learned from training data or computed using heuristics. The observation model is estimated thanks to an expectation maximization (EM) algorithm which extracts text and paperpsilas features. The performance of the proposition is evaluated on several types of degraded document images where considerable background noise or variation in contrast and illumination exist.