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PRL
2008

A large margin approach for writer independent online handwriting classification

9 years 10 months ago
A large margin approach for writer independent online handwriting classification
This paper proposes a new approach for classifying multivariate time-series with applications to the problem of writer independent online handwritten character recognition. Each time-series is approximated by a sum of piecewise polynomials in a suitably defined Reproducing Kernel Hilbert Space (RKHS). Using the associated kernel function a large margin classification formulation is proposed which can discriminate between two such functions belonging to the RKHS. The associated problem turns out to be an instance of convex quadratic programming. The resultant classification scheme applies to many time-series discrimination tasks and shows encouraging results when applied to online handwriting recognition tasks.
Karthik Kumara, Rahul Agrawal, Chiranjib Bhattacha
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PRL
Authors Karthik Kumara, Rahul Agrawal, Chiranjib Bhattacharyya
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