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16 January 2006 Toward quantifying the amount of style in a dataset
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Proceedings Volume 6067, Document Recognition and Retrieval XIII; 606707 (2006) https://doi.org/10.1117/12.651572
Event: Electronic Imaging 2006, 2006, San Jose, California, United States
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.
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Xiaoli Zhang and Srinivas Andra "Toward quantifying the amount of style in a dataset", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 606707 (16 January 2006); https://doi.org/10.1117/12.651572
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