A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting Recognition

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 207K collected cursive words written by 1060 subjects, the proposed algorithm clearly outperforms baseline

• Type: Conference
• Year: 2009
• Where: ICDAR
• Authors: Xiaoyuan Zhu, Yong Ge, Feng-Jun Guo, Li-Xin Zhen
• Keywords: Cursive Word Recognition, Cursive Words, Document Analysis, ICDAR 2009, Lower Bound