A grammatical inference approach to on-line handwriting modeling and recognition: a pilot study

Abstract

In this paper, we present a grammar-based approach to the modeling and recognition of temporal sequences. Unlike hidden Markov models which require humans to determine in advance the appropriate model architecture to work on, our approach does not rely on prior knowledge about the topology of the underlying grammars. In particular, a discrete-time recurrent neural network model is proposed to learn separately the dynamics of each embedded subgrammar (or subpattern) class. These subgrammar network models are trained using an unsupervised learning paradigm called auto-associative (or self-supervised) learning. In this pilot study, some issues of this new approach to temporal sequence processing are investigated in the domain of on-line handwriting modeling and recognition. Some possible future research directions are also discussed.