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Author (up) Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
  Title Sign Language Recognition: A Deep Survey Type Journal Article
  Year 2021 Publication Expert Systems With Applications Abbreviated Journal ESWA  
  Volume 164 Issue Pages 113794  
  Abstract 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.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ RKE2021a Serial 3521  
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