Voronoi cell shaping for feature selection with discrete HMMs

In this paper, we introduce a novel vector quantization (VQ) scheme for distributing the quantization error equally among the quantized dimensions. Afterwards, the proposed VQ scheme is used to perform feature selection in on-line handwritten whiteboard note recognition based on discrete Hidden-Markov-Models (HMMs). In an experimental section we show that the novel VQ scheme derives feature sets which contain less than 50% features, enabling recognition with better performance at less computational costs. Finally, the derived feature set is compared to the quantized features selected within a continuous HMM-based system: the features selected after quantization with the proposed VQ scheme are proved to perform significantly better than those in the continuous system.