Practical applications of online handwritten character recognition demand robust and highly accurate recognition along with low memory requirements. The Active-DTW [11] classifier proposed by Sridhar et al. combines the advantages of generative and discriminative classifiers to address the similarity of between-class samples, while taking into account the variability of writing styles within the same character class. Active-DTW uses Active Shape Models to model the significant writing styles in a memory efficient manner. However, in order to create accurate models, a large number of training samples is needed up front, which is not desirable or available in many practical applications. In this paper, we propose a supervised adaptation framework for the Active-DTW classifier which allows recognition to begin with a small number of training samples, and adapts the classifier to the new samples presented to the system during recognition. We compare the performance of Active-DTW using the proposed adaptation framework, with a nearest-neighbor classifier using an LVQ-based adaptation scheme, on the online handwritten Tamil character dataset.
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