Bayesian network scores based text localization in scene images

Abstract

Text localization in scene images is an essential and interesting task to analyze the image contents. In this work, a Bayesian network scores using K2 algorithm in conjunction with the geometric features based effective text localization method with the help of maximally stable extremal regions (MSERs). First, all MSER-based extracted candidate characters are directly compared with an existing text localization method to find text regions. Second, adjacent extracted MSER-based candidate characters are not encompassed into text regions due to strict edges constraint. Therefore, extracted candidate character regions are incorporated into text regions using selection rules. Third, K2 algorithm-based Bayesian networks scores are learned for the complimentary candidate character regions. Bayesian logistic regression classifier is built on the Bayesian network scores by computing the posterior probability of complimentary candidate character region corresponding to non-character candidates. The higher posterior probability of complimentary Candidate character regions are further grouped into words or sentences. Bayesian networks scores based text localization system, named as BayesText, is evaluated on ICDAR 2013 Robust Reading Competition (Challenge 2 Task 2.1: Text Localization) database. Experimental results have established significant competitive performance with the state-of-the-art text detection systems.