Hierarchical Classifiers Based On Neighbourhood Criteria With Adaptive Computational Cost
PRIS '01: Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems: In conjunction with ICEIS 2001(2002)
Abstract
Classifiers based on neighbourhood concept require a high computational cost when the Reference Patterns Set is large. In this paper, we propose the use of hierarchical classifiers to reduce this computational cost, maintaining the hit rate in the recognition of handwritten digits. The hierarchical classifiers reach the hit rate of the best individual classifier. We have used NIST Database to carry out the experimentation, and we have worked with two test sets: in Test 1 (SD3, SD19) the hit rate is 99.54%, with a speed-up of 40.6, and in Test 2 (SD7), the hit rate is 97.51% with a speed-up of 15.7. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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Key words
OCR,handwritten digits,hierarchical classifiers,k-NN classifier,k-NCN classifier
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