A MATLAB SMO Implementation to Train a SVM Classifier: Application to Multi-Style License Plate Numbers Recognition

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Communicated by Martín Rais
Demo edited by Martín Rais

Abstract

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.

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  Software Heritage Archive @softwareversion{sw-ipol.2018.173, title = {{A MATLAB SMO Implementation to Train a SVM Classifier: Application to Multi-Style License Plate Numbers Recognition}}, author = {Pablo Negri}, date = {2016-02-26}, license = {GPL-3.0-or-later}, version = {1.0}, swhid = {swh:1:dir:bdb828b015f6973ab499f8fc3bc6ab0b1cfe21d0;origin=https://doi.org/10.5201/ipol.2018.173;visit=swh:1:snp:c14476bd74cc920c88a1eedf5f955bee06078c09;anchor=swh:1:rel:02cd4cc195617b64943eb69d395e42576eb7c528}, related = {ipol.2018.173}, relatedtype = "Reference article: https://doi.org/10.5201/ipol.2018.173" } % the biblatex-software style extension is recommended for software bibitems
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Supplementary Materials

Dataset of license plate numbers from four countries having different fonts, and character/background colors: dataset

History

• Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.