Font-based persian character recognition using Simplified Fuzzy ARTMAP neural network improved by fuzzy sets and Particle Swarm Optimization

An evolutionary approach has been proposed to improve simplified fuzzy ARTMAP neural network performance for off-line font-based recognition of printed Persian alphabetical characters. Some of Persian characters are so similar to each other. We have defined and used some fuzzy sets in feature extraction to improve recognition of these characters. Also, the presentation order of training patterns to a simplified fuzzy ARTMAP neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. In this paper, a method based on particle swarm optimization is proposed to obtain the presentation order of training Persian fonts for improving the performance of simplified fuzzy ARTMAP. This method uses generalization error as a criterion to specify the best order of training patterns in this problem. The new method has the advantage of improved classification performance compared to the random ordering.The achieved average recognition rates were 91.24% for twelve popular Persian fonts.