A study of Japanese sign language recognition using human skeleton data

Alyssa Takazume; Noriko Yata; Yoshitsugu Manabe

doi:10.1117/12.2664992

25 March 2023 A study of Japanese sign language recognition using human skeleton data

Alyssa Takazume, Noriko Yata, Yoshitsugu Manabe

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125920X (2023) https://doi.org/10.1117/12.2664992
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

Sign language is a visual language that uses hand signs, arm movements and facial expressions to convey information. However, learning sign language is challenging task, due to variety of movements that should be executed precisely to express the correct information. Many studies have been made to recognize and translate the sign languages. Traditional methods face problems when used in a real-world situation due to the background and lighting variations. Recently, the result obtained from research using multi-modal data, including human skeleton data to Sign Language Recognition task (SLR), have archived a remarkable success.¹ This paper aims to make a model that uses only skeleton data to recognize sign language using Graph Convolutional Network (GCN) and make a Japanese Sign Language dataset to help future SLR research.

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Alyssa Takazume, Noriko Yata, and Yoshitsugu Manabe "A study of Japanese sign language recognition using human skeleton data", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125920X (25 March 2023); https://doi.org/10.1117/12.2664992

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KEYWORDS

RGB color model

Data modeling

Deep learning

Education and training

Convolution

Feature extraction

Analytical research

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