JISE


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Journal of Information Science and Engineering, Vol. 22 No. 5, pp. 1109-1123


American Sign Language Recognition Using Multi-dimensional Hidden Markov Models

Honggang Wang1, Ming C. Leu2 and Cemil Oz3
1Department of Industrial Engineering Purdue University 
West Lafayette, IN 47907, U.S.A. 
2Department of Mechanical and Aerospace Engineering 
University of Missouri-Rolla 
Rolla, MO 65409, U.S.A. 
3Department of Computer Engineering 
Sakarya University 
54187 Sakarya, Turkey

    An American Sign Language (ASL) recognition system developed based on multidimensional Hidden Markov Models (HMM) is presented in this paper. A CybergloveTM sensory glove and a Flock of BirdsR 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.

Keywords: American sign language, ASL recognition, handshape gestures, hidden Markov model, data glove, motion tracker

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