Robust text segmentation using graph cut

Text segmentation provides important clues for the accurate identification of character locations and the analysis of character properties such as shape estimation and texture synthesis. In this paper, we propose a robust text segmentation method that employs Markov Random Field (MRF) and use graph cut algorithms to solve the energy minimization problem. To effectively select accurate seeds to boost the text segmentation performance, stroke feature transform is adopted to robustly identify text seeds and text edges. Background seeds are obtained near the text edges in order to well preserve the text boundaries. The energy functions are defined as an MRF consisting of data energy and smoothness energy which can be efficiently solved by graph cut algorithms. One distinctive property of the proposed technique is that it can identify more distinctive seeds so that only one cut is needed to well separate the text regions from the background, hence much faster than the existing iterative graph cut approach. Experiments on ICDAR 2003 and ICDAR 2011 datasets show that the proposed technique obtains superior performance on both pixel level and atom level segmentation.