A novel method for embedded text segmentation based on stroke and color

Abstract—In this paper, a novel method for embedded text segmentation is proposed. The basic idea of our method is based on two properties of embedded texts: a) the color of text pixels is subject to gaussian distribution; b) the local part and the global part of embedded text shares the same color distribution. Inspired by this two characteristics, we develop a two-step text segmentation approach: in the coarse segmentation step, a 1-D gaussian function is adopted to model the color distribution of text pixels. To get the model parameters, a stroke operator is utilized to extract confident text region, and then a heuristic process is developed to estimate the parameters. The coarse segmentation can be carried out by the color model. In the noise elimination step, a color distribution homogeneity based method with connected component analysis is introduced. Pre-liminary experimental results show that our method performs well on complex background. Keywords-embedded text; text segment; stroke; color; I