Data-driven design of HMM topology for online handwriting recognition

Abstract

Although HMM is widely used for online handwriting recognition, there is no simple and well-established method of designing the HMM topology. We propose a data-driven systematic method to design HMM topology. Data samples in a single pattern class are structurally simplified into a sequence of straight-line 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 straight-line segments. Then the resulting multiple models of the class are combined to form an architecture of a multiple parallel-path HMM, which behaves as a single HMM. To avoid excessive growing of the number of the states, parameter tying is applied such that structural similarity among patterns is reflected. Experiments on online Hangul recognition showed about 19% of error reductions, compared to the intuitive design method.