Online handwriting recognition with support vector machines - a kernel approach
Tóm tắt
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.
Từ khóa
#Handwriting recognition #Support vector machines #Kernel #Hidden Markov models #Support vector machine classification #Nonlinear distortion #Pattern recognition #Optical character recognition software #Bayesian methods #Data structuresTài liệu tham khảo
watkins, 2000, Dynamic alignment kernels, Advances in Large Margin Classifiers, 39
10.1147/rd.266.0765
vuori, 2000, Adaptive character recognizer for a hand-held device: Implementation and evaluation setup, Proc 8th IWFHR, 13
rabiner, 1993, Fundamentals of speech recognition
10.1109/72.788641
platt, 2000, Large margin dags for multiclass classification, Advances in neural information processing systems
platt, 1999, Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods-Support Vector Learning Chapter 12, 185
jelinek, 1998, Statistical Methods for Speech Recognition
10.1109/ICDAR.2001.953836
cawley, 2000, MATLAB Support Vector Machine Toolbox (v0 50?
10.1023/A:1009715923555
bahlmann, 2001, Measuring hmmsimilarity with the bayes probability of error and its application to online handwriting recognition, Proc 8th ICDAR, 406
jaakkola, 1999, Using the Fisher kernel method to detect remote protein homologies, T Lengauer et Al Editors Proc 7th Int Conf on Intelligent Syst ForMolecular Biology (ISMB-99)
10.1109/ICPR.1994.576870
gauthier, 2001, Strategies for combining on-line and off-line information in an on-line handwriting recognition system, Proc 8th ICDAR, 412
10.1023/A:1012454411458
cristianini, 2000, Support Vector Machines
10.1016/S0031-3203(99)00043-6
10.1109/ICPR.2002.1048439