Improve the Recognition of Spoken Arabic Letter Based on Statistical Features

Authors

• Jabbar Salman, College of Engineering,University of Karbala, Karbala, IraqFollow
• Thamir Rashed Saeed, Department of Electrical Engineering, University of Technology, Baghdad, IraqFollow
• Alaa Hussein Ali, Department of Electrical Engineering, University of Technology, Baghdad, IraqFollow

Document Type

Original Study

Keywords

Control Engineering

Abstract

The recognition and classification of languages represent a vital factor in the computer 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.

How to Cite This Article

Salman, Jabbar; Saeed, Thamir Rashed; and Ali, Alaa Hussein (2018) "Improve the Recognition of Spoken Arabic Letter Based on Statistical Features," Iraqi Journal of Computers, Communications, Control and Systems Engineering: Vol. 18: Iss. 3, Article 3.
DOI: 10.33103/uot.ijccce.18.3.3
Available at: https://ijccce.researchcommons.org/journal/vol18/iss3/3