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Overlapped text segmentation using Markov random field and aggregation

Separating machine printed text and handwriting from overlapping text is a challenging problem in the document analysis field and no reliable algorithms have been developed thus far. In this paper, we propose a novel approach for separating handwriting from binary image of overlapped text. Instead of using fixed size training patches, we describe an aggregation method which uses shape context features to extract training samples automatically. We use a Markov Random Field (MRF) to model the overlapped text. The neighbor system is inherited from a coarsening procedure and the prior and likelihood of the MRF is learned based on a distance metric. Experimental results show that the proposed method can achieve 87.97% recall for handwriting and 91.44% recall for machine printed text.