Pattern theory for character recognition

Abstract

Pattern theory is an engineering theory of algorithm design which provides a robust characterization of all types of patterns. Similar to logical neural networks, the theory can be used to generalize from a set of training data. However, it optimizes network architectures as well as the "weights" of the resulting machine. In this paper, the application of the theory to character recognition is considered. The application requires a simple extension to the theory and a faster algorithm to perform a basic decomposition operation. Such an algorithm is developed and described in the paper. Some simulation results of the algorithm are also included.<>