Real-time American Sign Language recognition from video using hidden Markov models

Author

Starner, Thad ; Pentland, Alex

Author_Institution

Perceptual Comput. Sect., MIT, Cambridge, MA, USA

fYear

1995

fDate

21-23 Nov 1995

Firstpage

265

Lastpage

270

Abstract

Hidden Markov models (HMMs) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. We describe a real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers

Keywords

handicapped aids; hidden Markov models; image recognition; real-time systems; American Sign Language; American Sign Language recognition; HMM-based system; hand gestures; hidden Markov models; real-time; sign language; visual recognition; Face recognition; Fingers; Handicapped aids; Handwriting recognition; Hidden Markov models; Laboratories; Natural languages; Real time systems; Shape; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 1995. Proceedings., International Symposium on

Conference_Location

Coral Gables, FL

Print_ISBN

0-8186-7190-4

Type

conf

DOI

10.1109/ISCV.1995.477012

Filename

477012

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2552298