A statistical, nonparametric methodology for document degradation model validation
Kanungo, T.
Haralick, R.M.
Baird, H.S.
Stuezle, W.
Madigan, D.
Center for Autom. Res., Maryland Univ., College Park, MD, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Nov 2000
Volume: 22,
Issue: 11
On page(s): 1209- 1223
ISSN: 0162-8828
INSPEC Accession Number: 6786086
Digital Object Identifier: 10.1109/34.888707
Current Version Published: 2002-08-06
Abstract
Printing, photocopying, and scanning processes degrade the image quality of a document. Statistical models of these degradation processes are crucial for document image understanding research. In this paper, we present a statistical methodology that can be used to validate local degradation models. This method is based on a nonparametric, two-sample permutation test. Another standard statistical device, the power function, is then used to choose between algorithm variables such as distance functions. Since the validation and the power function procedures are independent of the model, they can be used to validate any other degradation model. A method for comparing any two models is also described. It uses p-values associated with the estimated models to select the model that is closer to the real world.
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