A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition

This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust...

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Detalles Bibliográficos
Publicado: 2018
Materias:
Histogram of oriented gradients
Multi-style license plate recognition
Sequential minimal optimization
Support vector machine
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_21051232_v8_n_p37_Negri
http://hdl.handle.net/20.500.12110/paper_21051232_v8_n_p37_Negri
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_21051232_v8_n_p37_Negri
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spelling paper:paper_21051232_v8_n_p37_Negri2023-06-08T16:34:20Z A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition Histogram of oriented gradients Multi-style license plate recognition Sequential minimal optimization Support vector machine This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions. © 2018 IPOL and the authors CC-BY-NC-SA. 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_21051232_v8_n_p37_Negri http://hdl.handle.net/20.500.12110/paper_21051232_v8_n_p37_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 Histogram of oriented gradients
Multi-style license plate recognition
Sequential minimal optimization
Support vector machine
spellingShingle Histogram of oriented gradients
Multi-style license plate recognition
Sequential minimal optimization
Support vector machine
A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
topic_facet Histogram of oriented gradients
Multi-style license plate recognition
Sequential minimal optimization
Support vector machine
description This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions. © 2018 IPOL and the authors CC-BY-NC-SA.
title A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_short A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_full A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_fullStr A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_full_unstemmed A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_sort matlab smo implementation to train a svm classifier: application to multi-style license plate numbers recognition
publishDate 2018
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_21051232_v8_n_p37_Negri
http://hdl.handle.net/20.500.12110/paper_21051232_v8_n_p37_Negri
_version_ 1768546276097392640

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