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Reseach Article

Recognition of Multi-font English Numerals using SOM Neural Network

by Hamid Hassanpour, Najmeh Samadiani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 4
Year of Publication: 2014
Authors: Hamid Hassanpour, Najmeh Samadiani

Hamid Hassanpour, Najmeh Samadiani . Recognition of Multi-font English Numerals using SOM Neural Network. International Journal of Computer Applications. 98, 4 ( July 2014), 37-41. DOI=10.5120/17174-7259

@article{ 10.5120/17174-7259,
author = { Hamid Hassanpour, Najmeh Samadiani },
title = { Recognition of Multi-font English Numerals using SOM Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 4 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { },
doi = { 10.5120/17174-7259 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:25:21.404846+05:30
%A Hamid Hassanpour
%A Najmeh Samadiani
%T Recognition of Multi-font English Numerals using SOM Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 4
%P 37-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

In this paper a new scheme is proposed for off-line recognition of multi-font numeral, using neural networks. Recognition of numerals has been a research area for many years because of its various applications. But there wasn't much research done for recognition of multi-font numerals. The approaches proposed so far, suffer from larger computation time and training because they must have a set of training samples per each font. They can be extended to recognize many more fonts but the accuracy decreases rapidly. So as to eliminate these drawbacks, in this paper, a method is presented which recognizes 30 different fonts of different sizes varying from size 10 to 28, with an accuracy of 99. 55% on a database of 2000 numeral images. The purpose of this study is to provide a new method to recognize digits based on neural network that can identify the same symbols after training without limitation on the type of the font. In the proposed method, a high accuracy rate is achieved in recognizing digits by extracting the appropriate features without the need for complex neural network architecture. This method uses a self-organizing map (SOM) neural network to measure similarity between the features of digits and the features of the indicators associated with the digits from 0 to 9 obtained in the training stage. In this method, one sample is used for each digit to train the network. So, the proposed method can be used to recognize typed letters without limitation on fonts.

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Index Terms

Computer Science
Information Sciences


OCR numeral recognition self-organizing map (SOM) similarity measure