Home // ACHI 2020, The Thirteenth International Conference on Advances in Computer-Human Interactions // View article
Authors:
Tomohiko Tsuchiya
Akihisa Shitara
Fumio Yoneyama
Nobuko Kato
Yuhki Shiraishi
Keywords: Sign language; Japanese fingerspelling; Sensor glove; Recognition; Convolutional neural network.
Abstract:
We have developed a Japanese fingerspelling recognition system based on a sensor glove using deep learning to achieve smooth communication between the deaf and hard-of-hearing and the hearing people. In this research, we conducted evaluation experiments using a convolutional neural network for 76 characters of Japanese fingerspelling. In the developed system, we adopt the sensor glove that is light and cheap. Besides, the target Japanese fingerspelling includes 35 characters of dynamic fingerspelling, which both finger and wrist have to be moved to express. Experimental results show that the average recognition rate is about 70.0%. Based on the results, we discuss the peculiarity of Japanese fingerspelling and the improvement of sensor gloves and algorithms.
Pages: 34 to 39
Copyright: Copyright (c) IARIA, 2020
Publication date: March 22, 2020
Published in: conference
ISSN: 2308-4138
ISBN: 978-1-61208-761-0
Location: Valencia, Spain
Dates: from November 21, 2020 to November 25, 2020