| Although HMM is widely used for online handwriting recognition, there is no simple and wellestablished way of designing the HMM topology. We propose a datadriven systematic method to design HMM topology. Data samples in a single pattern class are structurally simplified into a sequence of straightline segments, and then these simplified representations of the samples are clustered. An HMM is constructed for each of these clusters, by assigning a state to each straightline segments. Then the resulting multiple models of the class are combined to form an architecture of a multiple parallelpath HMM, which behaves as a single HMM. To avoid excessive growing of the number of the states, parameter tying is applied in that structural similarity among patterns is reflected. Experiments on online Hangul recognition showed about 19% of error reductions, compared to the previous intuitive design methods. |
In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 239-248.