Recognition of Arabic Characters using Spiking Neural Networks

被引：0
|
作者：
Humaidi, Amjad J. [1 ]
Kadhim, Thaer M. [1 ]
机构：
[1] Univ Technol Baghdad, Control & Syst Engn, Baghdad, Iraq
来源：
2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC) | 2017年
关键词：
Spiking Neural Network (SNN); Remote Supervised Method (ReSuMe); Artificial Neural Networks(AAN); MODEL;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Text Recognition is one of the active and challenging areas of research in the pattern recognition field. It covers many applications like automatic number plate recognition, bank cheques, aid of reading for blind, and handwritten document conversion into form of structural text. In the present work, the spiking neural network (SNN) model is utilized for the identification of Arabic characters in a character set. The structure of considered neural network consists of two layers with Izhekevich neurons. Remote Supervised Method (ReSuMe) has been used as a learning rule for training. Also, the recognition of Arabic characters has been considered based on spike Neural Networks. The network could successfully recognize character set consisting of 28 isolating Arabic characters.
引用
收藏
页码：7 / 11
页数：5
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