Handwritten digit recognition using an optimized nearest neighbor classifier

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

26 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)207-211
Journal / PublicationPattern Recognition Letters
Volume15
Issue number2
Publication statusPublished - Feb 1994
Externally publishedYes

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