A MSD-HMM Approach to Pen Trajectory Modeling for Online Handwriting Recognition

In modeling online handwritten characters, imaginary strokes have been conveniently generated by connecting adjacent real strokes together to form a continuous trajectory. However, this approach causes confusions among characters with similar but actually different trajectories. In this paper, we propose to use multi-space probability distribution (MSD) to model imaginary strokes jointly with real strokes. With the proposed MSD, real and imaginary strokes become observations from different probability spaces and they are modeled stochastically. Also, the flexibility in MSD to assign different feature dimensions to each individual space enables us to ignore certain features that can cause singularity problem in modeling. Experimental results obtained in handwritten Chinese character recognition indicate MSD provides 1.3%-2.8% character recognition accuracy improvement across different recognition systems where MSD significantly improves discrimination among confusable characters with similar trajectories.