The proposed modified Adaboost algorithm adopts the descriptive model based on multi-class classifiers (modified quadratic discriminant function, MQDF) as element classifiers which perform multi-class classification directly. It does not need to convert multi-class classifications to multiple binary classifications and has lower training complexity. Besides, it updates sample weights according to the generalized confidence which is simple and effective. In order to reduce the recognition complexity, the pruning method was performed to pick out only one best element classifier from all boosted classifiers to do the classification. Applying the algorithm to Chinese handwritten character recognition on HCL2000 and THOCR-HCD databases, the relative error rate reduced 14.3 %, 8.1 % and 19.5 % respectively.