Arabic sign language intelligent translator. (2nd January 2020)

• Record Type: Journal Article
• Authors: Ahmed, Abdelmoty M.; Abo Alez, Reda; Tharwat, Gamal; Taha, Muhammad; Belgacem, B.; Al Moustafa, Ahmad M. J.
• Is Part Of: Imaging science journal. Volume 68:Number 1(2020)
• Page Start: 11
• Page End: 23
• Publication Date: 2020-01-02
• Subjects: Arabic sign language -- feature extraction -- key frame extraction -- isolated dynamic gestures -- machine learning -- video processing -- gray of co-occurrence matrix (GLCM) -- intelligent translation system; Imaging systems -- Periodicals; 621.36705
• DOI: 10.1080/13682199.2020.1724438
• Languages: English
• ISSNs: 1368-2199

Abstract

ABSTRACT: Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.