An HMM-SNN Method for Online Handwriting Symbol Recognition

This paper presents a combined approach for online handwriting symbols recognition. The basic idea of this approach is to employ a set of left-right HMMs to generate a new feature vector as input, and then use SNN as a classifier to finally identify unknown symbols. The new feature vector consists of global features and several pairs of maximum probabilities with their associated different model labels for an observation pattern. A recogniser based on this method inherits the practical and dynamical modeling abilities from HMM, and robust discriminating ability from SNN for classification tasks. This hybrid technique also reduces the dimensions of feature vectors significantly, complexity, and solves size problem when using only SNN. The experimental results show that this approach outperforms several classifiers reported in recent research, and can achieve recognition rates of 97.41%, 91.81% and 91.63% for digits and upper/lower case characters respectively on the UNIPEN database benchmarks.