Character recognition using min-max classifiers designed via an LMS algorithm
Ping-Fai Yang, Petros A. Maragos · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
In this paper we propose a Least Mean Square (LMS) algorithm for the practical training of the class of min-max classifiers. These are lattice-theoretic generalization of Boolean functions and are also related to feed-forward neural networks and morphological signal operators. We applied the LMS algorithm to the problem of handwritten character recognition. The database consists of segmented and cleaned digits. Features that were extracted from the digits include Fourier descriptors and morphological shape-size histograms. Experimental results using the LMS algorithm for handwritten character recognition are promising. In our initial experimentation, we applied the min-max classifier to binary classification of '0' and '1' digits. By preprocessing the feature vectors, we were able to achieve an error rate of 1.75% for a training set of size 1200 (600 of each digit); and an error rate of 4.5% on a test set of size 400 (200 of each). These figures are comparable to those obtained by 2-layer neural nets trained using back propagation. The major advantage of min-max classifiers compared to neural networks is their simplicity and the faster convergence of their training algorithm.