A new ring radius transform-based thinning method for multi-oriented video characters

Thinning that preserves visual topology of characters in video is challenging in the field of document analysis and video text analysis due to low resolution and complex background. This paper proposes to explore ring radius transform (RRT) to generate a radius map from Canny edges of each input image to obtain its medial axis. A radius value contained in the radius map here is the nearest distance to the edge pixels on contours. For the radius map, the method proposes a novel idea for identifying medial axis (middle pixels between two strokes) for arbitrary orientations of the character. Iterative-maximal-growing is then proposed to connect missing medial axis pixels at junctions and intersections. Next, we perform histogram on color information of medial axes with clustering to eliminate false medial axis segments. The method finally restores the shape of the character through radius values of medial axis pixels for the purpose of recognition with the Google Open source OCR (Tesseract). The method has been tested on video, natural scene and handwritten characters from ICDAR 2013, SVT, arbitrary-oriented data from MSRA-TD500, multi-script character data and MPEG7 object data to evaluate its performances at thinning level as well as recognition level. Experimental results comparing with the state-of-the-art methods show that the proposed method is generic and outperforms the existing methods in terms of obtaining skeleton, preserving visual topology and recognition rate. The method is also robust to handle characters of arbitrary orientations.