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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. Source Code The source code is written in MATLAB and it is available at the IPOL web page of this article1. It implements Platt’s SMO algorithm to train and test a SVM classifier on a multi-style license plate character dataset. Compilation and usage instruction are included in the README.txt file of the archive. The online demo allows to test the character (numbers) recognition system, from license plate images. Supplementary Material The supplementary files of the work include a dataset of license plate numbers from four countries having different fonts, and character/background colors.

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