Persian Handwritten Digit Recognition Using Particle Swarm Probabilistic
Publish place: Journal of Iranian Association of Electrical and Electronics Engineers، Vol: 12، Issue: 3
Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JIAE-12-3_012
تاریخ نمایه سازی: 13 تیر 1396
Abstract:
Handwritten digit recognition can be categorized as 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. Hoda database, which includes 80000 Persian handwritten digit images, has been used to evaluate our our proposed classifier. Obtained results show that PNN is a powerful classifier 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%.
Keywords:
Probabilistic Neural Network (PNN) , Classification , Persian handwritten recognition , Particle swarm optimization , clustering , K-means
Authors
Mehran Taghipour-Gorjikolaie
Ph.D. Candidate, Faculty of Electrical & Computer Engineeing, University of Birjand, Birjand, Iran
Ismaeil Miri
Ph.D. Candidate, Faculty of Electrical & Computer Engineeing, University of Birjand, Birjand, Iran
Seyyed Mohammad Razavi
Associate Professor, Faculty of Electrical & Computer Engineering, University of Birjand, Birjand, Iran
Javad Sadri
Associate Professor, Department of Computer Science & Softwqre Engineering Faculty of Engineering and Computer Science Concordia University, Montreal, Quebec, Canada.