MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition

This paper has proposed a discriminative learning method of modified quadratic discriminant function (MQDF) based on sample importance weights. Firstly, sample importance function is derived from distance based recognition results under bayes decision rule. It weights samples according to extended recognition confidence. On these weighted samples, parameters of MQDF are modulated indirectly by re-estimating the mean vector and covariance matrix. The proposed method is investigated and compared with other discriminative learning methods about MQDF on THU-HCD offline Chinese handwriting sets. The results show that the proposed method has improved the basic MQDF drastically and outperforms other methods compared.