Multiple clues for license plate detection and 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...
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2010
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paper:paper_03029743_v6419LNCS_n_p269_Negri2023-06-08T15:28:37Z Multiple clues for license plate detection and recognition Tepper, Mariano Hernán Acevedo, Daniel G. Mejail, Marta Estela 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 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v6419LNCS_n_p269_Negri 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 Tepper, Mariano Hernán Acevedo, Daniel G. Mejail, Marta Estela 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. |
author |
Tepper, Mariano Hernán Acevedo, Daniel G. Mejail, Marta Estela |
author_facet |
Tepper, Mariano Hernán Acevedo, Daniel G. Mejail, Marta Estela |
author_sort |
Tepper, Mariano Hernán |
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 |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v6419LNCS_n_p269_Negri http://hdl.handle.net/20.500.12110/paper_03029743_v6419LNCS_n_p269_Negri |
work_keys_str_mv |
AT teppermarianohernan multiplecluesforlicenseplatedetectionandrecognition AT acevedodanielg multiplecluesforlicenseplatedetectionandrecognition AT mejailmartaestela multiplecluesforlicenseplatedetectionandrecognition |
_version_ |
1768542080857014272 |