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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 independent (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 adaptation technique so that we could generate natural-looking writer specific handwritings.