Neural net vector quantizers for discrete HMM-based on-line handwritten whiteboard-note recognition

In this work we evaluate a recently published vector quantization scheme, which has been developed to handle binary features like the pressure feature occurring in on-line handwriting recognition using discrete Hidden-Markov-Models (HMMs) with two neural net based vector quantizers (VQs). One of these uses a "Winner-Take-All" (WTA) update rule and the other implements the "Neural Gas" (NG) approach. Both approaches are believed to be more efficient VQs than the standard k-means VQ used in our earlier publication. In an experimental section we prove that both the WTA and NG neural net VQ significantly (significance is measured by the one-sided t-test) outperform our previously used k-means VQ by r_W = 0:9% and r_N = 0:8%, respectively, referring to word-level accuracy. In addition, no significant difference in recognition accuracy between the WTA-VQ and the NG-VQ could be observed.