Decision fusion for improved automatic license plate recognition

Authors:
Cristian Molder;Mircea Boscoianu;Iulian C. Vizitiu;Mihai I. Stanciu
Affiliations:
Military Technical Academy, Department of Electronics and Informatics, Bucharest, Romania;Military Technical Academy, Department of Electronics and Informatics, Bucharest, Romania;Military Technical Academy, Department of Electronics and Informatics, Bucharest, Romania;Military Technical Academy, Department of Electronics and Informatics, Bucharest, Romania
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2009

Citing 5
Cited 1

On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive license plate image extraction

CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Automatic plate detection using genetic algorithm

SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
A new version of Flusser moment set for pattern feature extraction

WSEAS Transactions on Information Science and Applications
Automatic license plate recognition

IEEE Transactions on Intelligent Transportation Systems

Lung area extraction from X-ray CT images for computer-aided diagnosis of pulmonary nodules by using active contour model

WSEAS Transactions on Information Science and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic license plate recognition (ALPR) is a pattern recognition application of great importance for access, traffic surveillance and law enforcement. Therefore many studies are concentrated on creating new algorithms or improving their performance. Many authors have presented algorithms that are based on individual methods such as skeleton features, neural networks or template matching for recognizing the license plate symbols. In this paper we present a novel approach for decisional fusion of several recognition methods, as well as new classification features. The classification results are proven to be significantly better than those obtained for each method considered individually. For better results, syntax corrections are also considered. Several trainable and non-trainable decisional fusion rules have been taken into account, evidencing each of the classification methods at their best. Experimental results are shown, the results being very encouraging by obtaining a symbol good recognition rate (GRC) of more than 99.4% on a real license plate database.