Document-zone classification using partial least squares and hybrid classifiers

Author

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

Author_Institution

Inst. for Adv. Studies, Univ. of Maryland, College Park, MD

fYear

2008

fDate

8-11 Dec. 2008

Firstpage

1

Lastpage

4

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.

Keywords

document image processing; feature extraction; image classification; least squares approximations; support vector machines; SVM classifier; document-zone classification algorithm; image feature extraction; one-against-all voting scheme; one-against-one voting scheme; partial least squares; Classification algorithms; Data mining; Educational institutions; Feature extraction; Image segmentation; Least squares methods; Optical character recognition software; Support vector machine classification; Support vector machines; Voting;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Conference_Location

Tampa, FL

ISSN

1051-4651

Print_ISBN

978-1-4244-2174-9

Electronic_ISBN

1051-4651

Type

conf

DOI

10.1109/ICPR.2008.4761553

Filename

4761553