Sign language interpretation using machine learning and artificial intelligence

Abstract

Abstract Sign language is the only way for deaf and mute people to represent their needs and feelings. Most of non-deaf-mute people do not understand sign language, which leads to many difficulties for deaf-mutes' communication in their social life. Sign language interpretation systems and applications get a lot of attention in the recent years. In this paper, we review sign language recognition and interpretation studies based on machine learning, image processing, artificial intelligence, and animation tools. The two reverse processes for sign language interpretation are illustrated. This study discusses the recent research on sign language translation to text and speech with the help of hand gestures, facial expressions interpretation, and lip reading. Also, state of the art in speech to sign language translation is discussed. In addition, some of the popular and highly rated Android and Apple mobile applications that facilitate disabled people communication are presented. This paper clarifies and highlights the recent research and real used applications for deaf-mute people help. This paper tries to provide a link between research proposals and real applications. This link can help covering any gap or non-handled functionalities in the real used applications. Based on our study, we introduce a proposal involves set of functionalities/options that separately introduced and discussed by the recent research studies. These recent research directions should be integrated for achieving more real help. Also, a set of non-addressed research directions are suggested for future focus.