Multiple clues for license plate detection and recognition

Mostrar todas las versiones(3)

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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Negri, P., Tepper, M., Acevedo, D., Jacobo, J., Mejail, M.
Formato: SER
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 todo:paper_03029743_v6419LNCS_n_p269_Negri
record_format dspace
spelling todo:paper_03029743_v6419LNCS_n_p269_Negri2023-10-03T15:19:16Z Multiple clues for license plate detection and recognition 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. SER 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)
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 SER
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
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_ 1782027338834247680

Ejemplares similares