The Community for Technology Leaders
2009 10th International Conference on Document Analysis and Recognition (2009)
Barcelona, Spain
July 26, 2009 to July 29, 2009
ISBN: 978-0-7695-3725-2
pp: 401-405
Practical applications of online handwritten character recognition demand robust and highly accurate recognition along with low memory requirements. The Active-DTW~\cite{activedtw} classifier proposed by Sridhar {\it 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.
Incremental Adaptation, Classifier, Online handwritten character recognition

S. Madhvanath, V. Roy, A. S. and R. R. Sharma, "A Framework for Adaptation of the Active-DTW Classifier for Online Handwritten Character Recognition," 2009 10th International Conference on Document Analysis and Recognition(ICDAR), Barcelona, Spain, 2009, pp. 401-405.
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