Efficient text localization in born-digital images by local contrast-based segmentation

Text localization in born-digital images is usually performed using methods designed for scene text images. Based on the observation that text strokes in born-digital images mostly have complete contours and the pixels on the contours have high contrast compared with the adjacent non-text pixels, we propose a method to extract candidate text components using local contrast. First, the image is segmented into smooth and non-smooth regions. After removing non-text smooth regions, the remaining smooth regions are merged with non-smooth regions to form a candidate text image, which is binarized into high-value and low-value connected components (CCs). The CCs undergo CC filtering, line grouping and line classification to give the text localization result. Experimental results on the born-digital dataset of ICDAR2013 robust reading competition demonstrate the efficiency and superiority of the proposed method.