Improving recognition rate of Persian handwritten digits classifier using FCM clustering and inclined planes system optimization algorithm

In this paper,a method is proposed to increase the recognition rate of the persian handwritten digits classifier. a fuzzy method is used for classification in which there is just one fuzzy rule for each digit [1]. the number of persian digits is 10,therefore,the total number of fuzzy rules used in the base method is 10. after implementation,where the number of fuzzy rules is ten,the recognition rate is 86.21 and 80.19 percent for training and testing samples,respectively. clustering is used to increase the recognition rate. training data related to each digit are divided into several clusters and one fuzzy rule is extracted for each cluster. thus,the number of fuzzy rules is significantly increased (784 rules),since the recognition rate is also increased significantly,increase of fuzzy rules is acceptable. by applying this method,the recognition rate of training and testing samples is increased to 98.97 and 97.15 percent,respectively. fcm method is used for clustering. to select the optimal number of clusters for each digit,the inclined planes system optimization algorithm is used.