AUTOMATIC ARABIC SIGN LANGUAGE RECOGNITION: A REVIEW, TAXONOMY, OPEN CHALLENGES, RESEARCH ROADMAP AND FUTURE DIRECTIONS

Abstract

Sign language is still the best communication mean between the deaf and hearing impaired citizens. Due to the advancements in technology, we are able to find various research attempts and efforts on Automatic Sign Language Recognition (ASLR) technology for many languages including the Arabic language. Such attempts have simplified and assisted the interpretation between spoken and sign languages. In fact, the technologies that translate between spoken and sign languages have become popular today. Being the first comprehensive and up-to-date review that studies the state-of-the-art ASLR in perspective to Arabic Sign Language Recognition (ArSLR), this review is a contribution to ArSLR research community. In this paper, the research background and fundamentals of ArSLR are provided. ArSLR research taxonomies, databases, open challenges, future research trends, and directions, and a roadmap to ArSLR research are presented. This review investigates two major taxonomies. The primary taxonomy that is related to the capturing mechanism of the gestures for ArSLR, which can be either a Vision-Based Recognition (VBR) approach or Sensor-Based Recognition (SBR) approach. The secondary taxonomy that is related to the type and task of the gestures for ArSLR, which can be either the Arabic alphabet, isolated words, or continuous sign language recognition. In addition, less research attempts have been directed towards Arabic continuous sign language recognition task compared to other tasks, which marks a research gap that can be considered by the research community. To the best of our knowledge, all previous research attempts and reviews on sign language recognition for ArSL used forehand signs. This shows that the backhand signs have not been considered for ArSL tasks, which creates another important research gap to be filled up. Therefore, we recommend more research initiatives to contribute to these gaps by using an SBR approach for signers' dependent and independent approaches.