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.


Mechanical and Aerospace Engineering


Ford Foundation
University of Missouri--Rolla. Intelligent Systems Center
National Science Foundation (U.S.)

Keywords and Phrases

ASL Recognition; Data Glove; Handshape Gestures; Motion Tracker; American Sign Language; Hidden Markov models

Document Type

Article - Journal

Document Version


File Type





© 2006 Institute of Information Science, All rights reserved.

Publication Date

01 Jan 2006