Radical based fine trajectory HMMs of online handwritten characters

We study models that characterize pen trajectories of online handwritten characters in a fine manner. We propose radical based fine trajectory hidden Markov models (HMMs), which adopt radicals as basic units, and a multi-path HMM topology that emits observations with multi-space distributions (MSD) is built for each radical. Meanwhile, various stroke orders, writing styles and realness of sub-strokes are reasonably modeled. The radical based fine trajectory HMMs lead to handwriting recognition with effective prediction, and their generative nature can be utilized for a novel handwriting synthesis framework. Experimental show that along with the model precision increasing, about 50% recognition error can be reduced, and the fine models can generate decent character samples.