DocumentCode
2834241
Title
Isolated sign language recognition using hidden Markov models
Author
Grobel, Kirsti ; Assan, Marcell
Author_Institution
Lehrstuhl fur Technische Inf., Tech. Hochschule Aachen, Germany
Volume
1
fYear
1997
fDate
12-15 Oct 1997
Firstpage
162
Abstract
This paper is concerned with the video-based recognition of isolated signs. Concentrating on the manual parameters of sign language, the system aims for the signer dependent recognition of 262 different signs. For hidden Markov modelling a sign is considered a doubly stochastic process, represented by an unobservable state sequence. The observations emitted by the states are regarded as feature vectors, that are extracted from video frames. The system achieves recognition rates up to 94%
Keywords
feature extraction; hidden Markov models; image recognition; image sequences; doubly stochastic process; feature vectors; hidden Markov models; isolated sign language recognition; signer dependent recognition; unobservable state sequence; video frames; video-based recognition; Arm; Cameras; Computer vision; Data gloves; Deafness; Handicapped aids; Hidden Markov models; Motion analysis; Speech; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location
Orlando, FL
ISSN
1062-922X
Print_ISBN
0-7803-4053-1
Type
conf
DOI
10.1109/ICSMC.1997.625742
Filename
625742
Link To Document