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In this paper, we compare three representative modeling approaches, namely the multiple-prototype-based template matching approach, the subspace approach and the continuous density hidden Markov model approach for large vocabulary offline recognition of handwritten Chinese characters. On a task of classification of 4616 handwritten Chinese characters, we evaluate and compare the strength and weakness of individual approaches in terms of the classification accuracy, the memory requirement and the computational complexity. We offer recommendations for practitioners on how to make intelligent use of these modeling approaches for different purposes in different applications.