Effective text localization in natural scene images with MSER, geometry-based grouping and AdaBoost

Text localization in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we proposed a novel and effective approach to accurately localize scene texts. Firstly, Maximally stable extremal regions(MSER) are extracted as letter candidates. Secondly, after elimination of non-letter candidates by using geometric information, candidate regions are constructed by grouping similar letter candidates using disjoint set. Candidate region features based on horizontal and vertical variances, stroke width, color and geometry are extracted. An AdaBoost classifier is built from these features and text regions are identified. The overall system is evaluated on the ICDAR 2011 competition dataset and the experimental results show that the proposed algorithm yields high precision and recall compared with the latest published algorithms.