Multiple clues for license plate detection and recognition

Autores
Negri, P.; Tepper, M.; Acevedo, D.; Jacobo, J.; Mejail, M.
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fuente
Lect. Notes Comput. Sci. 2010;6419 LNCS:269-276
Materia
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
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_03029743_v6419LNCS_n_p269_Negri
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oai_identifier_str paperaa:paper_03029743_v6419LNCS_n_p269_Negri
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Multiple clues for license plate detection and recognitionNegri, P.Tepper, M.Acevedo, D.Jacobo, J.Mejail, M.License plate detectionPixel valuesSegmentation algorithmsShape contextsStill imagesSVM classifiersLicense plate detectionOptical character recognition (OCR)Pixel valuesSegmentation algorithmsShape contextsStill imagesSVM classifiersAutomobilesClassifiersComputer visionFeature extractionCharacter recognitionImage segmentationLicense plates (automobile)Optical character recognitionOptical character recognitionPattern recognitionThis 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.2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_NegriLect. Notes Comput. Sci. 2010;6419 LNCS:269-276reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-04T09:48:21Zpaperaa:paper_03029743_v6419LNCS_n_p269_NegriInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-04 09:48:22.553Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Multiple clues for license plate detection and recognition
title Multiple clues for license plate detection and recognition
spellingShingle Multiple clues for license plate detection and recognition
Negri, P.
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
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
dc.creator.none.fl_str_mv Negri, P.
Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
author Negri, P.
author_facet Negri, P.
Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
author_role author
author2 Tepper, M.
Acevedo, D.
Jacobo, J.
Mejail, M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv 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
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
dc.description.none.fl_txt_mv 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.
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.
publishDate 2010
dc.date.none.fl_str_mv 2010
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri
url http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Lect. Notes Comput. Sci. 2010;6419 LNCS:269-276
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 12.623145

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