Multiple Classifier System for Offline Malayalam Character Recognition

被引：4
作者：
Chacko, Anitha Mary M. O. [1 ]
Dhanya, P. M. [1 ]
机构：
[1] Rajagiri Sch Engn & Technol, Dept Comp Sci & Engn, Kochi 682039, Kerala, India
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, ICICT 2014 | 2015年 / 46卷
关键词：
Character Recognition; Gradient feature; Density feature; Multiple Classifier System; and Neural Networks;
D O I：
10.1016/j.procs.2015.01.061
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a multiple classifier system for the recognition of offline handwritten Malayalam characters. The features used are the gradient and density based features. These feature sets are fed as input to two feedforward neural networks. The results of both these neural networks are combined using four different combination schemes: Max rule, Sum rule, Product rule and Borda count method. The best combination ensemble with an accuracy of 81.82% is obtained by using the Product rule combination scheme. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码：86 / 92
页数：7
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