Toward quantifying the amount of style in a dataset
Xiaoli Zhang, Srinivas Andra · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
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.