Turkish Journal of Electrical Engineering and Computer Sciences
DOI
10.3906/elk-1303-167
Abstract
The development of an American Sign Language (ASL) word recognition system based on neural networks and a probabilistic model is presented. We use a CyberGlove and a Flock of Birds motion tracker to extract the gesture data. The finger joint angle data obtained from the sensory glove defines the handshape while the data from the motion tracker describes the trajectory of the hand movement. The four gesture features, namely handshape, hand position, hand orientation, and hand movement, are recognized using different functions that include backpropagation neural networks. The sequence of these features is used to generate a specific sign or word in ASL based on a probabilistic model. The system can recognize the ASL signs in real time and update its database based interactively. The system has an accuracy of 95.4{\%} over a vocabulary of 40 ASL words.
Keywords
American Sign Language, American Sign Language recognition, virtual reality, artificial neural network, probabilistic model
First Page
2017
Last Page
2123
Recommended Citation
SARAWATE, NEELESH; LEU, MING CHAN.; and ÖZ, CEMİL
(2015)
"A real-time American Sign Language word recognition system based on neural networks and a probabilistic model,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 23:
No.
7, Article 6.
https://doi.org/10.3906/elk-1303-167
Available at:
https://journals.tubitak.gov.tr/elektrik/vol23/iss7/6
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
