Markov Random Field Based Text Identification from Annotated Machine Printed Documents

Xujun Peng, Srirangaraj Setlur, Venu Govindaraju, Ramachandrula Sitaram, Kiran Bhuvanagiri

Abstract: In this paper, we describe an approach to segment handwritten text, machine printed text and noise from annotated machine printed documents. Three categories of word level features are extracted. We use a modified K-Means clustering algorithm for classification followed by a relabeling procedure using Markov Random Field(MRF) based on a concept of neighboring patches and Belief Propagation(BP) rules. Experimental results on an imbalanced data set show that our approach achieves an overall recall of 96.33%.