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Text detection and localization in natural scene images using MSER and fast guided filter

Textual matter present in a natural scene image provides indispensable information about it. The semantics and information present in the natural scene images can be perceived by extracting the text regions in them. Detection and localization of text from natural scene images is a challenging task for analysis of images due to various font size, font type, and illumination. In this paper, we propose a hybrid approach for text detection and localization based on text confidence score using three attributes namely stroke width dissimilarity, color dissimilarity and occupy rate convex area to discern text and non-text constituents. The aim of this paper is to achieve fast detection and localization of text regions in low resolution and blurred images. To accomplish this, the possible candidate regions are extracted using edge smoothing by fast guided filter followed by MSER. The text confidence score on these constituents is calculated using the Bayesian framework with the help of above mentioned three attributes. Experimental results on benchmark ICDAR 2013 testing dataset shows the efficacy of our method in the form of precision, recall, and f-measure.