Modeling inverse covariance matrices by expansion of tied basis matrices for online handwritten Chinese character recognition

Authors:
Yongqiang Wang;Qiang Huo
Affiliations:
Microsoft Research Asia, MSRA, 5/F, Beijing Sigma Center, 49 Zhichun Road, Haidian District, Beijing 100190, PR China and Department of Computer Science, The University of Hong Kong, Hong Kong, Ch ...;Microsoft Research Asia, MSRA, 5/F, Beijing Sigma Center, 49 Zhichun Road, Haidian District, Beijing 100190, PR China
Venue:
Pattern Recognition
Year:
2009

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Abstract

The state-of-the-art modified quadratic discriminant function (MQDF) based approach for online handwritten Chinese character recognition (HCCR) assumes that the feature vectors of each character class can be modeled by a Gaussian distribution with a mean vector and a full covariance matrix. In order to achieve a high recognition accuracy, enough number of leading eigenvectors of the covariance matrix have to be retained in MQDF. This paper presents a new approach to modeling each inverse covariance matrix by basis expansion, where expansion coefficients are character-dependent while a common set of basis matrices are shared by all the character classes. Consequently, our approach can achieve a much better accuracy-memory tradeoff. The usefulness of the proposed approach to designing compact HCCR systems has been confirmed and demonstrated by comparative experiments on popular Nakayosi and Kuchibue Japanese character databases.