NDLI: Video-based sign recognition using self-organizing subunits

Content Provider	IEEE Xplore Digital Library
Author	Bauer, B. Kraiss, K.-F.
Copyright Year	2002
Description	Author affiliation: Dept. of Tech. Comput. Sci., Aachen Univ. of Technol., Germany (Bauer, B.; Kraiss, K.-F.)
Abstract	This paper deals with the automatic recognition of German signs. The statistical approach is based on the Bayes decision rule for minimum error rate. Following speech recognition system designs, which are in general based on phonemes, here the idea of an automatic sign language recognition system using subunits rather than models for whole signs is outlined. The advantage of such a system will be a future reduction of necessary training material. Furthermore, a simplified enlargement of the existing vocabulary is expected, as new signs can be added to the vocabulary database without re-training the existing hidden Markov models (HMMs) for subunits. Since it is difficult to define subunits for sign language, this approach employs totally self-organized subunits. In first experiences a recognition accuracy of 92,5% was achieved for 100 signs, which were previously trained. For 50 new signs an accuracy of 81% was achieved without retraining of subunit-HMMs.
Starting Page	434
Ending Page	437
File Size	258156
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048332
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handicapped aids Hidden Markov models Vocabulary Natural languages Speech recognition Computer science Computer errors Mouth Cameras Layout
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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