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Start Over Descriptor "BSL dataset"
3 results on '"BSL dataset"'

1. An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition

Author

Basnin, Nanziba, Nahar, Lutfun, Hossain, Mohammad Shahadat, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Mahmud, Mufti, editor, and Ray, Kanad, editor

Published
2021
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2. Real-time Bhutanese Sign Language digits recognition system using Convolutional Neural Network

Author

Karma Wangchuk, Panomkhawn Riyamongkol, and Rattapoom Waranusast

Subjects
Sign language, CNN, BSL dataset, Augmentation, Computer vision, Information technology, T58.5-58.64
Abstract

The communication gap between the deaf and public is the concern for both parents and the government of Bhutan. The deaf school urges people to learn Bhutanese Sign Language (BSL) but learning Sign Language (SL) is difficult. This paper presents the BSL digits recognition system using the Convolutional Neural Network (CNN) and a first-ever BSL dataset which has 20,000 sign images of 10 static digits collected from different volunteers. Different SL models were evaluated and compared with the proposed CNN model. The proposed system has achieved 97.62% training accuracy. The system was also evaluated with precision, recall, and F1-score.

Published
2021
Full Text
View/download PDF

3. Real-time Bhutanese Sign Language digits recognition system using Convolutional Neural Network

Author

Rattapoom Waranusast, Panomkhawn Riyamongkol, and Karma Wangchuk

Subjects
Recall, Computer Networks and Communications, Computer science, Speech recognition, 020208 electrical & electronic engineering, 020206 networking & telecommunications, 02 engineering and technology, Information technology, Sign language, Augmentation, T58.5-58.64, Convolutional neural network, Government (linguistics), BSL dataset, Artificial Intelligence, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Recognition system, Computer vision, Software, CNN, Information Systems, Sign (mathematics)
Abstract

The communication gap between the deaf and public is the concern for both parents and the government of Bhutan. The deaf school urges people to learn Bhutanese Sign Language (BSL) but learning Sign Language (SL) is difficult. This paper presents the BSL digits recognition system using the Convolutional Neural Network (CNN) and a first-ever BSL dataset which has 20,000 sign images of 10 static digits collected from different volunteers. Different SL models were evaluated and compared with the proposed CNN model. The proposed system has achieved 97.62% training accuracy. The system was also evaluated with precision, recall, and F1-score.

Published
2021

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