SignsWorld Atlas; a benchmark Arabic Sign Language database

• Published in: Journal of King Saud University. Computer and information sciences Vol. 27; no. 1; pp. 68 - 76
• Main Authors: Shohieb, Samaa M., Elminir, Hamdy K., Riad, A.M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2015; Elsevier
• Subjects: Arabic Sign Language; Manual signs; Non-manual signs; Sign language recognition; Sign language recognition; Database; Non-manual signs; Manual signs; Arabic Sign Language
• ISSN: 1319-1578; 2213-1248
• DOI: 10.1016/j.jksuci.2014.03.011

Research has increased notably in vision-based automatic sign language recognition (ASLR). However, there has been little attention given to building a uniform platform for these purposes. Sign language (SL) includes not only static hand gestures, finger spelling, hand motions (which are called manual signs “MS”) but also facial expressions, lip reading, and body language (which are called non-manual signs “NMS”). Building up a database (DB) that includes both MS and NMS is the main first step for any SL recognition task. In addition to this, the Arabic Sign Language (ArSL) has no standard database. For this purpose, this paper presents a DB developed for the ArSL MS and NM signs which we call SignsWorld Atlas. The postures, gestures, and motions included in this DB are collected in lighting and background laboratory conditions. Individual facial expression recognition and static hand gestures recognition tasks were tested by the authors using the SignsWorld Atlas, achieving a recognition rate of 97% and 95.28%, respectively.