Open AccessImprove the Recognition of Spoken Arabic Letter Based on Statistical Features
Author(s) -
Jabbar Salman,
Thamir R. Saeed,
Alaa H. Ali
Publication year - 2018
Publication title -
al-maǧallaẗ al-ʻirāqiyyaẗ li-handasaẗ al-ḥāsibāt wa-al-ittiṣālāt wa-al-sayṭaraẗ wa-al-naẓm
Language(s) - English
Resource type - Journals
eISSN - 2617-3352
pISSN - 1811-9212
DOI - 10.33103/uot.ijccce.18.3.3
Subject(s) - computer science , preprocessor , arabic , artificial intelligence , feature extraction , feature (linguistics) , speech recognition , artificial neural network , pattern recognition (psychology) , natural language processing , matlab , linguistics , philosophy , operating system
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-perform the earlier works. The simulation has been made by using Matlab 2015b.