Affiliations: [a] School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| [b] Technical College for Deaf, Tianjin University of Technology, Tianjin, China
Correspondence:
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Corresponding authors. Ming Yu, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China. E-mail: yuming@hebut.edu.cn; Cuihong Xue, Technical College for Deaf, Tianjin University of Technology, Tianjin, China. E-mail: redxuech@tjut.edu.cn.
Abstract: Sign language is the primary way of communication between hard-of-hearing and hearing people. Sign language recognition helps promote the better integration of deaf and hard-of-hearing people into society. We reviewed 95 types of research on sign language recognition technology from 1993 to 2021, analyzing and comparing algorithms from three aspects of gesture, isolated word, and continuous sentence recognition, elaborating the evolution of sign language acquisition equipment and we summarized the datasets of sign language recognition research and evaluation criteria. Finally, the main technology trends are discussed, and future challenges are analyzed.
Keywords: Sign language recognition, convolutional neural network, encoder-decoder, dataset
