Title- Improvement of Recognition Rate for Fuzzy Classification of Persian Handwritten Digits, Using K-means Clustering and Particle Swarm Optimization

In this paper a method is presented for improving fuzzy recognition of Persian Handwritten Digits. First the training samples for each digit are partitioned into several clusters, and then a fuzzy rule for each cluster is created. The number of clusters for each class (infect for each digit) is optimized using Particle Swarm Optimization (PSO) algorithm. Our approach can improve the recognition rate of a fuzzy algorithm that has only one rule for each digit. The proposed method has increased recognition rate on training data and testing data from 68. 21% and 61. 1 % to 16. 55% and 18. 21%, respectively.