کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531316 | 869827 | 2009 | 7 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Modeling inverse covariance matrices by expansion of tied basis matrices for online handwritten Chinese character recognition Modeling inverse covariance matrices by expansion of tied basis matrices for online handwritten Chinese character recognition](/preview/png/531316.png)
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.
Journal: Pattern Recognition - Volume 42, Issue 12, December 2009, Pages 3296–3302