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Proceedings Paper

Toward quantifying the amount of style in a dataset
Author(s): Xiaoli Zhang; Srinivas Andra
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Paper Abstract

Exploiting style consistency in groups of patterns (pattern fields) generated by the same source has been demonstrated to yield higher accuracies in OCR applications. The accuracy gains obtained by a style consistent classifier depend on the amount of style in a dataset in addition to the classifier itself. The computational complexity of style-based classifiers precludes their applicability in situations where datasets have small amounts of style. In this paper, we propose a correlation-based measure to quantify the amount of style in a dataset and demonstrate its use in determining the suitability of a style consistent classifier on both simulation and real datasets.

Paper Details

Date Published: 16 January 2006
PDF: 7 pages
Proc. SPIE 6067, Document Recognition and Retrieval XIII, 606707 (16 January 2006); doi: 10.1117/12.651572
Show Author Affiliations
Xiaoli Zhang, Rensselaer Polytechnic Institute (United States)
Srinivas Andra, 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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