A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images

In this paper, we present an adaptive method for the binarization of historical manuscripts and degraded document images. The proposed approach is based on maximum likelihood (ML) classification and uses a priori information and the spatial relationship on the image domain. In contrast with many conventional methods that use a decision based on thresholding, the proposed method performs a soft decision based on a probabilistic model. The main idea is that, from an initialization map (under-binarization) containing only the darkest part of the text, the method is able to recover the main text in the document image, including low-intensity and weak strokes. To do so, fast and robust local estimation of text and background features is obtained using grid-based modeling and inpainting techniques; then, the ML classification is performed to classify pixels into black and white classes. The advantage of the proposed method is that it preserves weak connections and provides smooth and continuous strokes, thanks to its correlation-based nature. Performance is evaluated both subjectively and objectively against standard databases. The proposed method outperforms the state-of-the-art methods presented in the DIBCO'09 binarization contest, although those other methods provide performance close to it.