A Real-Time American Sign Language Word Recognition System Based on Neural Networks and a Probabilistic Model
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.
N. Sarawate et al., "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, pp. 2107-2123, Turkiye Klinikleri Journal of Medical Sciences, Dec 2015.
The definitive version is available at https://doi.org/10.3906/elk-1303-167
Mechanical and Aerospace Engineering
Keywords and Phrases
Computational linguistics; Neural networks; Virtual reality; Vocabulary control; American sign language; Back propagation neural networks; Flock of Birds; Gesture features; Hand positions; Motion tracker; Probabilistic modeling; Word recognition; Gesture recognition; American sign language recognition; Artificial neural network; Probabilistic model
International Standard Serial Number (ISSN)
Article - Journal
© 2015 Turkiye Klinikleri Journal of Medical Sciences, All rights reserved.
01 Dec 2015