Abstract
In this chapter, we present an example of how pattern classification can be carried out in a digit recognition problem. There are ten classes corresponding to the handwritten digits ‘0’ to ‘9’. The data set consists of 6670 training patterns and 3333 test patterns. The nearest neighbour algorithm (NN), the kNN and the mkNN algorithms have been used on this data set. Classification is carried out for the test patterns and the classification accuracy reported.
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Bibliography
Devi, V. Susheela and M. Narasimha Murty. An incremental prototype set building technique. Pattern Recognition 35: 505–513. 2002.
Devi, V. Susheela and M. Narasimha Murty. Handwritten digit recognition using soft computing tools. In Soft Computing for Image Processing. Edited by S. K. Pal, A. Ghosh and M. K. Kundu. Berlin: Springer. 2000.
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© 2011 Universities Press (India) Pvt. Ltd.
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Murty, M.N., Devi, V.S. (2011). An Application: Handwritten Digit Recognition. In: Pattern Recognition. Undergraduate Topics in Computer Science, vol 0. Springer, London. https://doi.org/10.1007/978-0-85729-495-1_11
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DOI: https://doi.org/10.1007/978-0-85729-495-1_11
Publisher Name: Springer, London
Print ISBN: 978-0-85729-494-4
Online ISBN: 978-0-85729-495-1
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