Figure-ground discrimination and distortion-tolerant recognition of color characters in scene images

This paper proposes a new technique of figure-ground discrimination of color characters in scene images following two steps. The first step is temporary binarization by selecting one optimal projection axis in the RGB color space and a threshold value along the axis using Otsupsilas criterion as a two-class classification problem. The second step is figure-ground determination based on the figure-to-ground ratio on the image periphery and common characteristics that a character pattern should have. Next, regarding distortion-tolerant character recognition under the condition of a small sample size we compare our global affine transformation (GAT) correlation method against the well-known tangent distance, where both methods use only a single template for each of 62 alphanumeric characters. Experiments are made on a total of 698 character images extracted from the ICDAR 2003 robust OCR dataset. The proposed figure-ground discrimination method achieves a correct binarization rate of 75.3%. Next, in recognition of correctly binarized characters the GAT correlation method and the tangent distance realize correct recognition rates of 94.1% and 91.6%, respectively. Moreover, the GAT correlation method is found to outperform the tangent distance in robustness against rotation at an angle of more than 20 degrees.