Families of Markov models for document image segmentation

In this paper we compare several directed and undirected graphical models for different image segmentation problems in the domain of document image processing and analysis. We show that adapting the structure of the model to specific stations at hand, for instance character restoration, recto/verso separation and segmenting high resolution character images, can significantly improve segmentation performance. We propose inference algorithms for the different models and we test them on different data sets (manuscripts and printed text of different qualities).