TY - JOUR ID - TI - Improve the Recognition of Spoken Arabic Letter Based on Statistical Features AU - Alaa Hussein Ali AU - Thamir Rashed Saeed AU - Jabbar Salman PY - 2018 VL - 18 IS - 3 SP - 26 EP - 32 JO - IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم SN - 18119212 AB - The recognition and classification of languages represent a vital factor in thecomputer interaction. This paper presents Arabic Sign Language recognition, which isrepresented as an appealing application. The work in this paper is based on three steps;preprocessing, feature extraction and classification (Recognition). The statistical featureshave been used than the physical features, while Multilayer feed-forward neural networkas classification methods. The recognition percent is 96.33% has been gained over-performthe earlier works. The simulation has been made by using Matlab 2015b.

ER -