Nested state indexing in pairwise Markov networks for fast handwritten document image rule-line removal

The Markov random field (MRF) has been applied to modeling the connectivity constraints of the text in document images for tasks like binarization and rule-line removal. One challenge of applying the MRF is its high computational cost. This paper presents a method using two nested set of states trained to reduce the computational cost of patch-based MRF. The two sets of states are trained at different levels in coarse-to-fine order. We show effective reduction of run time but very little loss of quality using rule-line removal experiments.