Moment-Based Character-Normalization Methods Using a Contour Image Combined with an Original Image

Moment-based character-normalization methods are known to improve character-recognition accuracy. These methods use the moments of an input image, which has two dimensions because of the thickness of its stroke lines, to estimate transformation parameters, whereas character is essentially composed of one dimensional stroke lines. This implies that these methods overestimate the moments of the thick parts of character strokes. To solve this problem, moment-based normalization methods, which use the moments of a contour image of a character combined with the input image of that character, are proposed. To extract the contours of character strokes, two methods, chain code contour (CC) and gradient contour (GC), are used. Character-recognition experiments on two printed-character databases and on two handwritten-character databases show that the character-recognition accuracies of the proposed methods are comparable to or significantly higher than those of conventional methods. In particular, the proposed methods are more effective for printed-character recognition.