Computationally evaluating and synthesizing Chinese calligraphy.

We present an approach for synthesizing Chinese calligraphy with a similar topological style from learning author′s written works. Our first contribution is an algorithm to match the trajectory. Second contribution is a method to represent Chinese character topology via WF-histogram. Third contribution is an algorithm to take topological features as features and feed them into the evaluation model—that is Adaboost composed of support vector regressions (SVRs). Fourth contribution is a Genetic Algorithm (GA) introduced in the optimization glyph phase. Moreover, we introduce hypothesis testing and the decay function of transformation amplitude to improve the converge speed. The experiments demonstrate that our approach can obtain a similar topological style Chinese calligraphy with training samples.