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A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript

A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript

Behnam Zebardast, Isa Maleki
Copyright: © 2013 |Volume: 4 |Issue: 4 |Pages: 16
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466635562|DOI: 10.4018/ijaec.2013100105
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MLA

Zebardast, Behnam, and Isa Maleki. "A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript." IJAEC vol.4, no.4 2013: pp.72-87. http://doi.org/10.4018/ijaec.2013100105

APA

Zebardast, B. & Maleki, I. (2013). A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript. International Journal of Applied Evolutionary Computation (IJAEC), 4(4), 72-87. http://doi.org/10.4018/ijaec.2013100105

Chicago

Zebardast, Behnam, and Isa Maleki. "A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript," International Journal of Applied Evolutionary Computation (IJAEC) 4, no.4: 72-87. http://doi.org/10.4018/ijaec.2013100105

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Abstract

During recent decades, recognizing letters was a considerable discussion for artificial intelligence researchers and recognize letters due to the variety of languages and different approaches have many challenges. The Artificial Neural Networks (ANNs) are framed based on particular application such as recognition pattern and data classification through learning process is configured. So, it is a proper approach to recognize letters. Kurdish language has two popular handwritings based on Arabic and Latin. In this paper, Radial Basis Function (RBF) of ANNs is used to recognize Kurdish-Latin manuscripts. Although, the authors' proposed method is also used to recognize the letters of all Latin languages which include English, Turkish and etc. are used. The authors implement RBF of ANNs in MATLAB environment. In this paper, the efficiency criteria is supposed to minimize the Mean Square Error (MSE) to recognize Kurdish letters and maximize recognition accuracy of Kurdish letters in training and testing stage of RBF of ANNs. The recognition accuracy in training and testing stages are 100% and 96.7742%, respectively.

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