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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Writer Dependent Online Handwriting Generation with Bayesian Network
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Hyunil Choi, Korea Advanced Institute of Science and Technology
Sung Jung Cho, Samsung Advanced Institute of Technology
Jin H. Kim, Korea Advanced Institute of Science and Technology
In this paper, we propose a method to generate writer dependent (WD) handwritings. We modelled the shape of character both globally and locally with probabilistic relationships between character components. Then writer indendent (WI) model was trained with lots of data. Once WI model was built, the model was adapted to a training example to maximize likelihood of the example by minimization of squared error between model and instance. The experimental results of WI numeral character generation showed that global shape consistencies and variabilities of local shape were preserved. The relationships from WI model were still valid in WD models by proposed adpatation technique so that we could generate natural-looking writer specific handwritings.
Citation:
Hyunil Choi, Sung Jung Cho, Jin H. Kim, "Writer Dependent Online Handwriting Generation with Bayesian Network," iwfhr, pp.130-135, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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