Document-zone classification using Partial Least Squares and hybrid classifiers

Wael Abd-Almageed; Mudit Agrawal; Wontaek Seo; David Doermann

doi:10.1109/icpr.2008.4761553

Document-zone classification using Partial Least Squares and hybrid classifiers

Wael Abd-Almageed
, Mudit Agrawal
, Wontaek Seo
, David Doermann

Department of Computer Science and Engineering

University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and Partial Least Squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against- all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3% classification accuracy is obtained.

Original language	English
Title of host publication	2008 19th International Conference on Pattern Recognition, ICPR 2008
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781424421756
DOIs	https://doi.org/10.1109/icpr.2008.4761553
State	Published - 2008

Publication series

Name	Proceedings - International Conference on Pattern Recognition
ISSN (Print)	1051-4651

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10.1109/icpr.2008.4761553

Cite this

Abd-Almageed, W., Agrawal, M., Seo, W., & Doermann, D. (2008). Document-zone classification using Partial Least Squares and hybrid classifiers. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 Article 4761553 (Proceedings - International Conference on Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icpr.2008.4761553

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title = "Document-zone classification using Partial Least Squares and hybrid classifiers",

abstract = "This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and Partial Least Squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against- all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3\% classification accuracy is obtained.",

author = "Wael Abd-Almageed and Mudit Agrawal and Wontaek Seo and David Doermann",

year = "2008",

doi = "10.1109/icpr.2008.4761553",

language = "English",

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Abd-Almageed, W, Agrawal, M, Seo, W & Doermann, D 2008, Document-zone classification using Partial Least Squares and hybrid classifiers. in 2008 19th International Conference on Pattern Recognition, ICPR 2008., 4761553, Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/icpr.2008.4761553

Document-zone classification using Partial Least Squares and hybrid classifiers. / Abd-Almageed, Wael; Agrawal, Mudit; Seo, Wontaek et al.
2008 19th International Conference on Pattern Recognition, ICPR 2008. Institute of Electrical and Electronics Engineers Inc., 2008. 4761553 (Proceedings - International Conference on Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Document-zone classification using Partial Least Squares and hybrid classifiers

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AU - Agrawal, Mudit

AU - Seo, Wontaek

AU - Doermann, David

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Y1 - 2008

N2 - This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and Partial Least Squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against- all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3% classification accuracy is obtained.

AB - This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and Partial Least Squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against- all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3% classification accuracy is obtained.

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DO - 10.1109/icpr.2008.4761553

M3 - Conference contribution

AN - SCOPUS:77957965137

SN - 9781424421756

T3 - Proceedings - International Conference on Pattern Recognition

BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Document-zone classification using Partial Least Squares and hybrid classifiers

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