Optimizing the Number of States for HMM-Based On-line Handwritten Whiteboard Recognition

In this paper, we present a novel way to determine the number of states in Hidden-Markov-Models for on-line handwriting recognition. This method extends the Bakis length modeling method which has successfully been applied to off-line handwriting recognition. We propose a modification to the Bakis method and present a technique to improve the topology with a small number of iterations. Furthermore, we investigate the influence of state tying. In an experimental section, we show that our improved system outperforms a system with Bakis length modeling by 1.5 % relative and with fixed length modeling by 5.1 % relative on the IAM-On-DB-t1 benchmark.