Multiple clues for license plate detection and recognition

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This paper addresses a license plate detection and recognition (LPR) task on still images of trucks. The main contribution of our LPR system is the fusion of different segmentation algorithms used to improve the license plate detection. We also compare the performance of two kinds of classifiers for...

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Detalles Bibliográficos
Autores principales: Negri, P., Tepper, M., Acevedo, D., Jacobo, J., Mejail, M.
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2010
Materias:
License plate detection
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
Optical character recognition (OCR)
Automobiles
Classifiers
Computer vision
Feature extraction
Character recognition
Image segmentation
License plates (automobile)
Optical character recognition
Pattern recognition
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paperaa:paper_03029743_v6419LNCS_n_p269_Negri
record_format dspace
spelling paperaa:paper_03029743_v6419LNCS_n_p269_Negri2023-06-12T16:47:20Z Multiple clues for license plate detection and recognition Lect. Notes Comput. Sci. 2010;6419 LNCS:269-276 Negri, P. Tepper, M. Acevedo, D. Jacobo, J. Mejail, M. License plate detection Pixel values Segmentation algorithms Shape contexts Still images SVM classifiers License plate detection Optical character recognition (OCR) Pixel values Segmentation algorithms Shape contexts Still images SVM classifiers Automobiles Classifiers Computer vision Feature extraction Character recognition Image segmentation License plates (automobile) Optical character recognition Optical character recognition Pattern recognition This paper addresses a license plate detection and recognition (LPR) task on still images of trucks. The main contribution of our LPR system is the fusion of different segmentation algorithms used to improve the license plate detection. We also compare the performance of two kinds of classifiers for optical character recognition (OCR): one based on the a contrario framework using the shape contexts as features and the other based on a SVM classifier using the intensity pixel values as features. © 2010 Springer-Verlag. Fil:Tepper, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Acevedo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2010 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
topic License plate detection
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
License plate detection
Optical character recognition (OCR)
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
Automobiles
Classifiers
Computer vision
Feature extraction
Character recognition
Image segmentation
License plates (automobile)
Optical character recognition
Optical character recognition
Pattern recognition
spellingShingle License plate detection
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
License plate detection
Optical character recognition (OCR)
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
Automobiles
Classifiers
Computer vision
Feature extraction
Character recognition
Image segmentation
License plates (automobile)
Optical character recognition
Optical character recognition
Pattern recognition
Negri, P.
Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
Multiple clues for license plate detection and recognition
topic_facet License plate detection
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
License plate detection
Optical character recognition (OCR)
Pixel values
Segmentation algorithms
Shape contexts
Still images
SVM classifiers
Automobiles
Classifiers
Computer vision
Feature extraction
Character recognition
Image segmentation
License plates (automobile)
Optical character recognition
Optical character recognition
Pattern recognition
description This paper addresses a license plate detection and recognition (LPR) task on still images of trucks. The main contribution of our LPR system is the fusion of different segmentation algorithms used to improve the license plate detection. We also compare the performance of two kinds of classifiers for optical character recognition (OCR): one based on the a contrario framework using the shape contexts as features and the other based on a SVM classifier using the intensity pixel values as features. © 2010 Springer-Verlag.
format Artículo
Artículo
publishedVersion
author Negri, P.
Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
author_facet Negri, P.
Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
author_sort Negri, P.
title Multiple clues for license plate detection and recognition
title_short Multiple clues for license plate detection and recognition
title_full Multiple clues for license plate detection and recognition
title_fullStr Multiple clues for license plate detection and recognition
title_full_unstemmed Multiple clues for license plate detection and recognition
title_sort multiple clues for license plate detection and recognition
publishDate 2010
url http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri
work_keys_str_mv AT negrip multiplecluesforlicenseplatedetectionandrecognition
AT tepperm multiplecluesforlicenseplatedetectionandrecognition
AT acevedod multiplecluesforlicenseplatedetectionandrecognition
AT jacoboj multiplecluesforlicenseplatedetectionandrecognition
AT mejailm multiplecluesforlicenseplatedetectionandrecognition
_version_ 1769810031219310592

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