Title :
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
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;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761553
