Abstract
Sign language is a way by which deaf and dumb people can express their thoughts and feelings. Deaf and dumb people use hand shape, body movements, and facial expressions for communication. In this work, a vision-based Indian Sign Language Recognition system using a convolutional neural network (CNN) is implemented. The sign images are captured by a USB camera. To segment the hand region, background subtraction method is used. Two databases have been created for ISL. Each of the databases contains 26 alphabets having a total of 52,000 images. The system has been also tested in real time, where the sign alphabets by four signers in front of the camera are captured and it correctly recognized almost all of the signs. Accuracy of around 99.40% is obtained.
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Sarkar, A., Talukdar, A.K., Sarma, K.K. (2020). CNN-Based Real-Time Indian Sign Language Recognition System. In: Chillarige, R., Distefano, S., Rawat, S. (eds) Advances in Computational Intelligence and Informatics. ICACII 2019. Lecture Notes in Networks and Systems, vol 119. Springer, Singapore. https://doi.org/10.1007/978-981-15-3338-9_9
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DOI: https://doi.org/10.1007/978-981-15-3338-9_9
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