American Sign Language Recognition Using Multi-Dimensional Hidden Markov Models
An American Sign Language (ASL) recognition system developed based on multi-dimensional Hidden Markov Models (HMM) is presented in this paper. A Cyberglove™ sensory glove and a Flock of Birds® motion tracker are used to extract the features of ASL gestures. The data obtained from the strain gages in the glove defines the hand shape while the data from the motion tracker describes the trajectory of hand movement. Our objective is to continuously recognize ASL gestures using these input devices in real time. with the features extracted from the sensory data, we specify multi-dimensional states for ASL signs in the HMM processor. The system gives an average of 95% correct recognition for the 26 alphabets and 36 basic handshapes in the ASL after it has been trained with 8 samples. New gestures can be accommodated in the system with an interactive learning processor. The developed system forms a sound foundation for continuous recognition of ASL full signs.
H. Wang et al., "American Sign Language Recognition Using Multi-Dimensional Hidden Markov Models," Journal of Information Science and Engineering, Institute of Information Science, Jan 2006.
Mechanical and Aerospace Engineering
University of Missouri--Rolla. Intelligent Systems Center
National Science Foundation (U.S.)
Keywords and Phrases
ASL Recognition; Data Glove; Handshape Gestures; Motion Tracker
Library of Congress Subject Headings
American Sign Language
Hidden Markov models
Article - Journal
© 2006 Institute of Information Science, All rights reserved.