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

Author

Wang, Yongqiang ; Huo, Qiang

Author_Institution

Microsoft Res. Asia, Beijing

fYear

2008

fDate

8-11 Dec. 2008

Firstpage

1

Lastpage

5

Abstract

This paper presents a new approach to large-vocabulary 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-the-art 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.

Keywords

covariance analysis; handwritten character recognition; natural languages; vocabulary; Kuchibue Japanese character database; Nakayosi database; STC model parameter; maximum likelihood error; minimum classification error; online handwritten Chinese character recognition; semitied covariance modeling; vocabulary; Asia; Automatic speech recognition; Character recognition; Computer science; Databases; Eigenvalues and eigenfunctions; Handwriting recognition; Hidden Markov models; Linear discriminant analysis; Maximum likelihood estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Conference_Location

Tampa, FL

ISSN

1051-4651

Print_ISBN

978-1-4244-2174-9

Electronic_ISBN

1051-4651

Type

conf

DOI

10.1109/ICPR.2008.4761547

Filename

4761547