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IJE TRANSACTIONS B: Applications Vol. 30, No. 11 (November 2017) 1538-1547    Article in Press

PDF URL: http://www.ije.ir/Vol30/No11/B/26.pdf  
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  A MODFIED SOM NEURAL NETWORK TO RECOGNIZE MULTI-FONT PRINTED PERSIAN NUMERALS (RESEARCH NOTE)
 
H. Hassanpour, N. Samadiani and F. Akbarzadeh
 
( Received: July 31, 2017 – Accepted: September 08, 2017 )
 
 

Abstract    Abstract: This paper deals with a new method to distinguish the printed digits, without regard to font and size, using neural networks.Unlike our proposed method, existing neural network based techniques are only able to recognize the trained fonts. These methods need a large database containing digits in various fonts. New fonts are often introduced to the public, which may not be truly recognized by the Optical Character Recognition (OCR). Therefore, the existing OCR systems may need to be retrained or their algorithm be updated. In this paper we propose a self-organizing map (SOM) neural network powered by appropriate features to achieve high accuracy rate for recognizing printed digits problem. In this method we use a limited sample size for each digit in training step. Two expriments are designed to evaluate the performance of the proposed method. First, we used the method to classify a database including 2000 printed Persian samples with twenty different fonts and ten different sizes from which 98.05% accuracy was achieved. Second, the proposed method is applied to unseen data with different fonts and sizes with those used in training data set. The results show 98% accuracy in recognizing unseen data.

 

Keywords    Keywords: Recognition; multi-font; similarity measure; self-organizing map

 

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