Accurate recognition of blurred images is a practical but hitherto mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adaps to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods, and has impressive ability to recognize blurred image patterns.

discriminant function, Mahalanobis distance, Bayes classifier, distribution of feature vectors, noise, blurred character recognition