Persian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network

Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hodadatabase, which includes 80000 Persian handwritten digit images, has been used to evaluate our proposed classifier. Obtained results show that PNN is a powerfulclassifier and excellent choice for classification of Persian handwritten digits. Correct recognition rate when training and testing data have been used directly (without clustering) for training data is 100% and for testing data is 96%, but when k-means has been used as cluster tool and clusters' center have been used as training data, in this case, correct recognition rate for training data is 100% and for testing data is 96.16%. In addition, when Particle Swarm Optimization (PSO) has been used to find optimum clusters for each class of Persian handwritten digits, correct recognition rate in training data is 100% and for the testing data it reaches to 98.18%.

Probabilistic Neural Network (PNN), Classification,Persian handwritten digit recognition, Particle swarm optimization, clustering, K-means.