Decision fusion for improved automatic license plate recognition

  • Authors:

  • Mircea Boscoianu;Cristian Molder;Janel Arhip;Mihai I. Stanciu;Lulian C. Vizitiu

  • 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;Military Technical Academy, Department of Electronics and Informatics, Bucharest, Romania

  • Venue:
  • MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
  • Year:
  • 2008

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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 that each of the method considered individually. For better results, syntax corrections are also considered. Several trainable and nontrainable 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.