Handwritten digit recognition using an optimized nearest neighbor classifier
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 207-211 |
Journal / Publication | Pattern Recognition Letters |
Volume | 15 |
Issue number | 2 |
Publication status | Published - Feb 1994 |
Externally published | Yes |
Link(s)
Abstract
In this paper we present a method for handwritten digit recognition using a nearest neighbor classifier. In this method a set of prototypes are obtained from the training samples and used to build a nearest neighbor classifier. The classifier is then mapped to a multi-layer perceptron. After training the neural network is mapped back to a nearest neighbor classifier with new and optimized prototypes. Using this method we were able to obtain recognition accuracy from 93.7% with 100 prototypes to 96.2% with 500 prototypes for samples not used for training. © 1994.
Research Area(s)
- handwritten character recognition, multi-layer neural network, Nearest neighbor classifier, prototype optimization
Citation Format(s)
Handwritten digit recognition using an optimized nearest neighbor classifier. / Yan, Hong.
In: Pattern Recognition Letters, Vol. 15, No. 2, 02.1994, p. 207-211.
In: Pattern Recognition Letters, Vol. 15, No. 2, 02.1994, p. 207-211.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review