NDLI: Supervised hidden Markov modeling for on-line handwriting recognition

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Supervised hidden Markov modeling for on-line handwriting recognition

Content Provider	IEEE Xplore Digital Library
Author	Bellegarda, J.R. Nahamoo, D. Nathan, K.S. Bellegarda, E.J.
Copyright Year	1994
Description	Author affiliation: IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA (Bellegarda, J.R.; Nahamoo, D.; Nathan, K.S.; Bellegarda, E.J.)
Abstract	The performance of a large alphabet handwriting recognition system based on a probabilistic framework is critically tied to the quality of the prototype distributions that are established in the relevant feature space. To better account for handwriting variability, we describe a supervised strategy for the construction of prototype distributions which are more robust to allograph deformations. The idea is to incorporate supervision to relate the allographic models to their manifestations in the feature space. This makes for a better utilization of the available training data, while at the same time allowing for a short design time turn around. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.<>
File Size	402377
File Format	PDF
ISBN	0780317750
ISSN	15206149
DOI	10.1109/ICASSP.1994.389426
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Handwriting recognition Prototypes Training data Speech recognition Robustness Concatenated codes Writing Tin Graphics
Content Type	Text
Resource Type	Article

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