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Proceedings Paper

Style consistent nearest neighbor classifier
Author(s): Srinivas Andra; Xiaoli Zhang
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Paper Abstract

Most pattern classifiers are trained on data from multiple sources, so that they can accurately classify data from any source. However, in many applications, it is necessary to classify groups of test patterns, with patterns in each group generated by the same source. The co-occurring patterns in a group are statistically dependent due to the commonality of source. The dependence between these patterns introduces style context within a group that can be exploited to improve the classification accuracy. In this paper, we present a style consistent nearest neighbor classifier that exploits style context in groups of adjacent patterns to improve the classification accuracy. We demonstrate the efficacy of the proposed classifier on a dataset of machine-printed digits where the proposed classifier reduces the error rate by 64.5%.

Paper Details

Date Published: 16 January 2006
PDF: 6 pages
Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670P (16 January 2006); doi: 10.1117/12.643570
Show Author Affiliations
Srinivas Andra, Rensselaer Polytechnic Institute (United States)
Xiaoli Zhang, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 6067:
Document Recognition and Retrieval XIII
Kazem Taghva; Xiaofan Lin, Editor(s)

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