Improve the Recognition of Spoken Arabic Letter Based on Statistical Features
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING,
2018, Volume 18, Issue 3, Pages 26-32
Abstract
The recognition and classification of languages represent a vital factor in thecomputer interaction. This paper presents Arabic Sign Language recognition, which is
represented as an appealing application. The work in this paper is based on three steps;
preprocessing, feature extraction and classification (Recognition). The statistical features
have been used than the physical features, while Multilayer feed-forward neural network
as classification methods. The recognition percent is 96.33% has been gained over-perform
the earlier works. The simulation has been made by using Matlab 2015b.
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