A Framework for Adaptation of the Active-DTW Classifier for Online Handwritten Character Recognition
Roy, Vandana; Madhvanath, Sriganesh; S., Anand; Sharma, Raghunath R.
Keyword(s): online handwritten character recognition, classifier, adaptation, Active-DTW
Abstract: Practical applications of online handwritten character recognition demand robust and highly accurate recognition along with low memory requirements. The Active-DTW  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.
Additional Publication Information: Published in International Conference on Document Analysis and Recognition, Barcelona, August 2009.
External Posting Date: September 21, 2009 [Fulltext]. Approved for External Publication
Internal Posting Date: September 21, 2009 [Fulltext]