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
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
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=artiaex&d=paper_03029743_v6419LNCS_n_p269_Negri_oai
Aporte de:
Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires
Descripción
Sumario: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.

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