Toward quantifying the amount of style in a dataset

Xiaoli Zhang; Srinivas Andra

doi:10.1117/12.651572

16 January 2006 Toward quantifying the amount of style in a dataset

Xiaoli Zhang, Srinivas Andra

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available