TY - JOUR AU - Razieh Rastgoo AU - Kourosh Kiani AU - Sergio Escalera PY - 2021// TI - Sign Language Recognition: A Deep Survey T2 - ESWA JO - Expert Systems With Applications SP - 113794 VL - 164 N2 - Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. UR - https://doi.org/10.1016/j.eswa.2020.113794 N1 - HUPBA; no proj;MILAB ID - Razieh Rastgoo2021 ER -