NDLI: Tree-based state clustering using self-organizing principles for large vocabulary on-line handwriting recognition

Content Provider	IEEE Xplore Digital Library
Author	Kosmala, A. Rigoll, G.
Copyright Year	1998
Description	Author affiliation: Dept. of Comput. Sci., Gerhard-Mercator-Univ., Duisburg, Germany (Kosmala, A.)
Abstract	The introduction of trigraphs offers a powerful method for the accuracy enhancement of handwriting modeling. A trigraph is a hidden Markov model (HMM) for a special character with defined adjacent characters. Especially in large vocabulary systems, as they are investigated here, the number of unseen trigraphs for which no training samples are available, exceeds the number of seen trigraphs by far. This paper presents a novel approach, which allows a synthesis of unseen trigraphs from seen trigraphs. With the method proposed here, a mean relative error reduction of 42% was obtained on a writer dependent system, resulting in an overall word recognition rate of 94.1%.
Starting Page	1313
Ending Page	1315
File Size	150311
Page Count	3
File Format	PDF
ISBN	0818685123
ISSN	10514651
DOI	10.1109/ICPR.1998.711943
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-08-20
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Hidden Markov models Handwriting recognition Text recognition Writing Frequency Training data Computer science Power system modeling Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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Tree-based state clustering using self-organizing principles for large vocabulary on-line handwriting recognition