Effectively localize text in natural scene images

In this paper, we present an effective approach to locate scene text in images based on connected components analysis (CCA). Our approach first utilizes a multi-scale adaptive local thresholding operator to convert an image into two complementary binary images. Then, connected components (CCs) are extracted from both of them, which ensures that bright or dark text in contrast to background can be detected. Further, some rules are designed based on stroke features to verify whether a connected component belongs to characters, and the obtained candidate components are further checked on the word level by using a graph to represent spatial relation of different components. Finally, scene text regions are localized by searching the collinear maximum group over the graph. The comparison experiments of the proposed method with some representative state-of-the-art methods, on the challenging dataset ICDAR 2003, show that the proposed approach is very effective, and it is robust to text of different sizes, fonts, colors, as well as orientation of text lines.