Circle Text Expansion as Low-Rank Textures

Circle ring aligned text is very common in our daily life, such as university logo, advertisement, there are quite few methods to expand the circle text which will greatly improve the working range of optical character recognition (OCR) product and the accuracy of text segmentation. In this paper, a new method is proposed to handle this circumstance, called curve Transform Invariant Low-rank Textures(TILT). By change the Cartesian system into polar system, the transformed image matrix D can be decomposed into low-rank matrix A and a sparse error matrix E. Matrix A represent the text expansion image and E is the noises and other non-regular component of text image. All this consist of an optimized convex problem and can be solved by alternating direction method (ADM) method. The proposed method also provides a frame work for curve text expansion. Extensive experiments show the robustness of proposed method in expanding artificial and real text image, which contain English or Chinese texts.