Text localization in natural scene images by mean-shift clustering and parallel edge feature
Huy Phat Le, Nguyen Dinh Toan, Sang‐Cheol Park, Guee-Sang Lee · 2011
A new text localization method using the parallel edge feature of text strokes is proposed, based on the observation that text-stroke consists of two edges in parallel. First, mean-shift clustering is employed to group similar pixels into clusters. The connected components in each cluster are considered as candidates for text strokes. Then, parallel edges are detected to verify whether the connected components are text strokes. The contribution of this paper is the presentation of a new feature of parallel edges along the stroke, providing structural information for the text localization. The performance, evaluated on ICDAR2003 image database, shows that the proposed algorithm works successfully with most of the text images.