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A New Approach to Persian and Arabic Handwritten Character Recognition with Hybrid of Artificial Neural Network and Genetic Algorithm

Ezzat Ahmad Zade, Wahab Amini Azar, Mohammad Masdari Published in Pattern Recognition

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451116
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  1. Ezat Ahmadzadeh, Wahab Amini Azar and Mohammad Masdari. Article: A New Approach to Persian and Arabic Handwritten Character Recognition with Hybrid of Artificial Neural Network and Genetic Algorithm. International Journal of Applied Information Systems 6(9):11-15, March 2014. BibTeX

    @article{key:article,
    	author = "Ezat Ahmadzadeh and Wahab Amini Azar and Mohammad Masdari",
    	title = "Article: A New Approach to Persian and Arabic Handwritten Character Recognition with Hybrid of Artificial Neural Network and Genetic Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 6,
    	number = 9,
    	pages = "11-15",
    	month = "March",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Handwritten character recognition systems using automated pattern recognition is one of the important issues in the field of Information Technology. In this paper, proposed a method based on combining artificial neural networks and genetic algorithms to recognize handwritten of the Persian and Arabic OFF-LINE characters. As the neural network searches for the optimal values of weights and biases of different layers the researcher used an intelligent genetic optimization algorithm to find optimal values. After preprocessing and feature extraction operation, BITMAP image as a file system entries from 10 different characters, each with 40 samples from Persian manuscript characters. A total of 400 different samples. 80% of samples are used for training (320 samples) and 20% of samples (80 samples) are used for network testing. Because there are lots of common ground between Arabic and Persian alphabet, Persian handwritten character recognition method is also applicable to the detection of Arabic words. The proposed method does not depend on a particular language and method, so it can be employed to recognize letters in different languages . It can also be used to identify letters' typed in a variety of languages. MSE obtained results of the combination of artificial neural networks and genetic algorithms showed that the proposed method is one of the best methods to employ in the field of pattern recognition.

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Keywords

Handwritten character recognition, neural networks, genetic algorithms, pattern recognition.