Improvement of artificial neural network based character recognition system, using SciLab

被引：8
|
作者：
Priyadarshni [1 ]
Sohal, J. S. [1 ]
机构：
[1] LCET Katani Kalan, Ludhaina, India
来源：
OPTIK | 2016年 / 127卷 / 22期
关键词：
Self organizing maps; K means clustering; SciLab; Character recognition;
D O I：
10.1016/j.ijleo.2016.05.106
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this paper a particular emphasis is given on developing a character recognition system using SciLab, a free and open source computing software and is most promising alternative to MatLab. In the proposed work a character recognition system to extract printed text from an image is developed using Kohenen self organizing maps (SOM) based retrieval system. SOM being an unsupervised method of training has a superior feature extracting property. Samples of same characters which are oriented at same angle but with different size, color and fonts are used. After calculation of certain topological and geometrical properties of a character it is classified and recognized. With self organizing map together with K means clustering algorithm using SciLab software, the system has achieved a remarkable accuracy of 99% to 100%, when tested for various text input images. (C) 2016 Published by Elsevier GmbH.
引用
收藏
页码：10510 / 10518
页数：9
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