Fast scene text localization by learning-based filtering and verification

This paper proposes a new method for fast text localization in natural scene images by combining learning-based region filtering and verification in a coarse-to-fine strategy. In each pyramid layer, a boosted region filter is used to extract candidate text regions, which are segmented into candidate text lines by multi-orientation projection analysis. A polynomial classifier with combined features is used to verify patches of candidate text lines for removing non-texts. The remaining text patches over all pyramid layers are grouped into text lines based on their spatial relationships. The text lines are further refined and partitioned into words by connected component analysis. Experimental results show that the proposed method provides competitive localization performance at high speed.