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To develop effective learning algorithms for online cursive word recognition is still a challenge research issue. In this paper, we propose a probabilistic framework to model the inherent ambiguity of cursive handwriting by using soft target vector of each character class. In the proposed algorithm, the values of soft targets are estimated by introducing a lower bound on the log likelihood and optimizing this lower bound via an EM like algorithm. In the experiments on 207 K collected cursive words written by 1060 subjects, the proposed algorithm clearly outperforms baseline method with word error reduction up to 11.6%. Furthermore, the estimated soft target values are useful for measuring the separability between output classes.