Scene Text Extraction by Superpixel CRFs Combining Multiple Character Features

Features and relationships based on character color, edge, stroke and context plays a role for text extraction in natural scene images, but any single feature or relationship is not enough to do the job. This paper presents a novel approach for combining features and relationships within the Conditional Random Field (CRF) framework. By a simple homogeneity measure, an input image is over segmented into perceptually meaningful super pixels and then the text extraction task is formulated as a problem of super pixel labeling. Such a formulation allows us to achieve parameter learning from training images and probabilistic inferences by combining all the features and relationships of the input image. The proposed method shows high performance, in terms of quality, on both the KAIST scene text DB and the ICDAR 2003 DB.