Optical character correction of large-curvature annular sector text in polar coordinate system

Abstract

Optical character recognition (OCR) of complex morphologies represented by large-curvature annular sector text (AST) is a very challenging task. A three-segment text recognition framework consisting of detection, correction and recognition is currently an effective method for dealing with complex morphological OCR. Optical character correction (OCC) is a key component in processing largecurvature AST. This paper proposes an OCC method in the polar coordinate system, which consists of control point preprocessing, polar coordinate transformation, and image remapping. The control point preprocessing is used to normalize the control points of the large curvature AST region; the polar coordinate transformation is to convert the pixels in the rectangular coordinate system to the polar coordinate system; image remapping maps the original image in polar coordinate system to polar coordinate space for re-representation. The method proposed in this paper can be used in conjunction with most detection and recognition modules and is applicable to any language type. Furthermore, the correction process consumes very little computational resources and has little impact on the speed of text detection and recognition. Experimental results show that the proposed method outperforms state-of-the-art algorithms in large curvature AST correction and recognition experiments