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ICPR
2008
IEEE

A study of semi-tied covariance modeling for online handwritten Chinese character recognition

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A study of semi-tied covariance modeling for online handwritten Chinese character recognition
This paper presents a new approach to largevocabulary online handwritten Chinese character recognition based on semi-tied covariance (STC) modeling. Detailed procedures are described for estimating the STC model parameters under both maximum likelihood (ML) and minimum classification error (MCE) criteria. Compared with the state-of-theart modified quadratic discriminant function (MQDF) based classifiers, STC-based classifiers can achieve a better memory-accuracy trade-off, thus provide more flexibility in designing compact online handwritten Chinese character recognizers. Its usefulness has been confirmed and demonstrated by comparative experiments on popular Nakayosi and Kuchibue Japanese character databases.
Yongqiang Wang, Qiang Huo
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Yongqiang Wang, Qiang Huo
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