NDLI: Neural net vector quantizers for discrete HMM-based on-line handwritten whiteboard-note recognition

Content Provider	IEEE Xplore Digital Library
Author	Schenk, J. Rigoll, G.
Copyright Year	2008
Description	Author affiliation: Inst. for Human-Machine Commun., Tech. Univ. Munchen, Munich (Schenk, J.; Rigoll, G.)
Abstract	In this work we evaluate a recently published vector quantization scheme, which has been developed to handle binary features like the pressure feature occurring in on-line handwriting recognition using discrete Hidden-Markov-Models (HMMs) with two neural net based vector quantizers (VQs). One of these uses a ldquoWinner-Take-Allrdquo (WTA) update rule and the other implements the ldquoNeural Gasrdquo (NG) approach. Both approaches are believed to be more efficient VQs than the standard k-means VQ used in our earlier publication. In an experimental section we prove that both the WTA and NG neural net VQ significantly (significance is measured by the one-sided t-test) outperform our previously used k-means VQ by $r_{W}$ = 0:9% and $r_{N}$ = 0:8%, respectively, referring to word-level accuracy. In addition, no significant difference in recognition accuracy between the WTA-VQ and the NG-VQ could be observed.
Starting Page	1
Ending Page	4
File Size	439123
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761448
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Handwriting recognition Hidden Markov models Vector quantization Automatic speech recognition Feature extraction Data mining Man machine systems Standards publication Gaussian processes
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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