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Online handwriting recognition with support vector machines - a kernel approach | IEEE Conference Publication | IEEE Xplore

Online handwriting recognition with support vector machines - a kernel approach


Abstract:

In this paper we describe a novel classification approach for online handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machine...Show More

Abstract:

In this paper we describe a novel classification approach for online handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel. This kernel approach has a main advantage over common HMM techniques. It does not assume a model for the generative class conditional densities. Instead, it directly addresses the problem of discrimination by creating class boundaries and thus is less sensitive to modeling assumptions. By incorporating DTW in the kernel function, general classification problems with variable-sized sequential data can be handled. In this respect the proposed method can be straightforwardly applied to all classification problems, where DTW gives a reasonable distance measure, e.g., speech recognition or genome processing. We show experiments with this kernel approach on the UNIPEN handwriting data, achieving results comparable to an HMM-based technique.
Date of Conference: 06-08 August 2002
Date Added to IEEE Xplore: 07 November 2002
Print ISBN:0-7695-1692-0
Conference Location: Niagra-on-the-Lake, ON, Canada

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