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.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Yongqiang Wang, Qiang Huo
• Keywords: Chinese Character, Chinese Character Recognition, Computer Vision, ICPR 2008, STC Model Parameters