[IPOL announce] new article: A MATLAB SMO Implementation to Train a SVM Classifier: Application to Multi-Style License Plate Numbers Recognition

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2018/173/

Pablo Negri,
A MATLAB SMO Implementation to Train a SVM Classifier: Application to 
Multi-Style License Plate Numbers Recognition,
Image Processing On Line, 8 (2018), pp. 51–70.
https://doi.org/10.5201/ipol.2018.173

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.