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Authors: Mehmet Ahat ; Cagdas Ulas and Onur Agin

Affiliation: Yapi Kredi Bank, Turkey

ISBN: 978-989-758-054-3

Keyword(s): Document Image Retrieval and Classification, SVM, One-Versus-All, AdaBoost, ECOC, BoVW Model.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Data Manipulation ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing and Artificial Vision Applications ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

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.

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Paper citation in several formats:
Ahat, M.; Ulas, . 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

@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},
}

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, .
AU - Agin, O.
PY - 2014
SP - 250
EP - 255
DO - 10.5220/0005131502500255

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