Hidden Markov Models for Online Handwritten Tamil Word Recognition

Abstract

Hidden Markov models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, hidden Markov models are increasingly being used to model substrokes of characters. However, when it comes to Indie script recognition, the published work employing HMMs is limited, and generally focussed on isolated character recognition. In this effort, a data-driven HMM-based online handwritten word recognition system for Tamil, an Indie script, is proposed. The accuracies obtained ranged from 98% to 92.2% with different lexicon sizes (IK to 20 K words). These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indie scripts as well.