Document Image Classification Via AdaBoost and ECOC Strategies Based on SVM Learners

Mehmet Ahat, Cagdas Ulas, Onur Agin

Abstract

In this paper, we describe easily extractable features and an approach for document image retrieval and classification at spatial level. The approach is based on the content of the image and utilizing visual similarity, it provides high speed classification of noisy text document images without optical character recognition (OCR). Our method involves a bag-of-visual words (BoVW) model on the designed descriptors and a Random- Window (RW) technique to capture the structural relationships of the spatial layout. Using the features based on these information, we analyze different multiclass classification methods as well as ensemble classifiers method with Support Vector Machine (SVM) as a base learner. The results demonstrate that the proposed method for obtaining structural relations is competitive for noisy document image categorization.

References

  1. Allwein, E., Schapire, R., and Singer, Y. (2001). Reducing multiclass to binary: A unifying approach for margin classifiers. J. Mach. Learn. Res., 1:113-141.
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Paper Citation


in Harvard Style

Ahat M., Ulas C. and Agin O. (2014). Document Image Classification Via AdaBoost and ECOC Strategies Based on SVM Learners . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 250-255. DOI: 10.5220/0005131502500255


in Bibtex Style

@conference{ncta14,
author={Mehmet Ahat and Cagdas Ulas and Onur Agin},
title={Document Image Classification Via AdaBoost and ECOC Strategies Based on SVM Learners},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={250-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005131502500255},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Document Image Classification Via AdaBoost and ECOC Strategies Based on SVM Learners
SN - 978-989-758-054-3
AU - Ahat M.
AU - Ulas C.
AU - Agin O.
PY - 2014
SP - 250
EP - 255
DO - 10.5220/0005131502500255