Multicategory Learning Classifiers for Character Reading

This paper presents properties of several different algorithms suitable for multicategory classification of hand-printed alphanumeric characters. In the character reader the input patterns are generally composed of the template characters and their distorted ones. Using the template patterns, a nonparametric procedure is developed for determining linear discriminant functions. Furthermore, we propose the mechanism which has the ability to recognize even a misprinted character by using the information of the preceding character. The algorithms offer the following advantages: flexibility (cost assignments), simplicity, adaptation, and acceptable performance. Performance of the machines is analyzed and convergence proofs of the learning procedures in the machines are derived. We also present some results of computer experiments.