Arabic Character Recognition using Artificial Neural Networks and Statistical Analysis

被引：0
|
作者：
Sarhan, Ahmad M. [1 ]
Al Helalat, Omar I. [1 ]
机构：
[1] New York Inst Technol, Abu Dhabi, U Arab Emirates
来源：
PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 21 | 2007年 / 21卷
关键词：
ANN; Backpropagation; Gaussian; LMS; MSE; Neuron; standard deviation; Widrow-Hoff rule;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.
引用
收藏
页码：32 / +
页数：3
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