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Authors: Dong-jin Lee 1 and Ho-sub Yoon 2

Affiliations: 1 University of Science and Technology, Korea, Republic of ; 2 Electronics and Telecommunications Research Institute, Korea, Republic of

ISBN: 978-989-8565-33-4

Keyword(s): Sign Recognition, Character Recognition, Hybrid HMM/SVM, Feature Extraction, Natural Scene Images.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: In this paper, we propose a sign classification system to recognize exit number and arrow signs in natural scene images. The purpose of the sign classification system is to provide assistance to a visually-handicapped person in subway stations. For automatically extracting sign candidate regions, we use Adaboost algorithm, however, our detector not only extracts sign regions, but also non-sign (noise) regions in natural scene images. Thus, we suggest a verification technique to discriminate sign regions from non-sign regions. In addition, we suggest a novel feature extraction algorithm cooperated with Hidden Markov Model. To evaluate the system, we tested a total of 20,177 sign candidate regions including the number of 8,414 non-sign regions on the captured images under several real environments in Daejeon in South Korea. We achieved an exit number and arrow sign recognition rate of each 99.5% and 99.8% and a false positive rate (FPR) of 0.3% to discriminate between sign regions and n on-sign regions. (More)

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Paper citation in several formats:
Lee, D. and Yoon, H. (2012). Sign Recognition with HMM/SVM Hybrid for the Visually-handicapped in Subway Stations.In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 631-634. DOI: 10.5220/0004155006310634

@conference{ncta12,
author={Dong{-}jin Lee. and Ho{-}sub Yoon.},
title={Sign Recognition with HMM/SVM Hybrid for the Visually-handicapped in Subway Stations},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={631-634},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004155006310634},
isbn={978-989-8565-33-4},
}

TY - CONF

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Sign Recognition with HMM/SVM Hybrid for the Visually-handicapped in Subway Stations
SN - 978-989-8565-33-4
AU - Lee, D.
AU - Yoon, H.
PY - 2012
SP - 631
EP - 634
DO - 10.5220/0004155006310634

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