DocumentCode :
1415572
Title :
A statistical, nonparametric methodology for document degradation model validation
Author :
Kanungo, Tapas ; Haralick, Robert M. ; Baird, Henry S. ; Stuezle, Werner ; Madigan, David
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD, USA
Volume :
22
Issue :
11
fYear :
2000
fDate :
11/1/2000 12:00:00 AM
Firstpage :
1209
Lastpage :
1223
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.
Keywords :
document image processing; optical character recognition; parameter estimation; statistical analysis; document degradation model; model validation; nonparametric statistical test; optical character recognition; parameter estimation; simulation models; statistical models; two-sample permutation test; Algorithm design and analysis; Control system synthesis; Degradation; Electric breakdown; Image quality; Optical character recognition software; Optimal control; Predictive models; System performance; Testing;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.888707
Filename :
888707
Link To Document :
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