Affine-invariant gray-scale character recognition using GAT correlation

This paper describes a new technique of gray-scale character recognition that offers both noise-tolerance and affine-invariance. The key ideas are twofold. First is the use of normalized cross-correlation to realize noise-tolerance. Second is the application of global affine transformation (GAT) to the input image so as to achieve affine-invariant correlation with the target image. In particular, optimal GAT is efficiently determined by the successive iteration method. We demonstrate the high matching ability of the proposed method using gray-scale images of numerals subjected to random Gaussian noise and a wide range of affine transformation. The achieved recognition rate of 92.1% against rotation within 30 degrees, scale change within 30%, and translation within 20% of the character width is sufficiently high compared to the 42.0% offered by simple correlation.