GMs in On-Line Handwritten Whiteboard Note Recognition: The Influence of Implementation and Modeling

Abstract

We present a comparison of two state-of-the-art toolboxes for implementing Graphical Models (GMs), namely the HTK and the GMTK, and their use for discrete on-line handwritten whiteboard note recognition. We then motivate a GM that is capable of modeling the statistical dependencies between the pen’s pressure information and the remaining features after vector quantization. Since the number of variable parameters rises when more codebook entries are used for quantization, the proposed model outperforms standard HMMs for low numbers of codebook entries.