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A cognitive and video-based approach for multinational License Plate Recognition

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Abstract

License Plate Recognition (LPR) is mainly regarded as a solved problem. However, robust solutions able to face real-world scenarios still need to be proposed. Country-specific systems are mostly, designed, which can (artificially) reach high-level recognition rates. This option, however, strictly limits their applicability. In this paper, we propose an approach that can deal with various national plates. There are three main areas of novelty. First, the Optical Character Recognition (OCR) is managed by a hybrid strategy, combining statistical and structural algorithms. Secondly, an efficient probabilistic edit distance is proposed for providing an explicit video-based LPR. Last but not least, cognitive loops are introduced at critical stages of the algorithm. These feedback steps take advantage of the context modeling to increase the overall system performances, and overcome the inextricable parameter settings of the low-level processing. The system performances have been tested in more than 1200 static images with difficult illumination conditions and complex backgrounds, as well as in six different videos containing 525 moving vehicles. The evaluations prove our system to be very competitive among the non-country specific approaches.

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Authors and Affiliations

  1. Laboratoire d’Informatique de Paris 6, UMR CNRS 7606, Université Pierre & Marie Curie, 104 avenue du président Kennedy, 75016, Paris, France

    Nicolas Thome

  2. Université de Lyon, CNRS, Université Lyon 2, LIRIS, UMR5205, 5 avenue Pierre Mendès France, 69676, Bron Cedex, France

    Nicolas Thome, Antoine Vacavant, Lionel Robinault & Serge Miguet

  3. Foxstream SA, 5 avenue Pierre Mendès France, 69676, Bron Cedex, France

    Nicolas Thome & Lionel Robinault

Authors
  1. Nicolas Thome
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  2. Antoine Vacavant
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  3. Lionel Robinault
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  4. Serge Miguet
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Correspondence to Nicolas Thome.

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Thome, N., Vacavant, A., Robinault, L. et al. A cognitive and video-based approach for multinational License Plate Recognition. Machine Vision and Applications 22, 389–407 (2011). https://doi.org/10.1007/s00138-010-0246-3

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  • DOI: https://doi.org/10.1007/s00138-010-0246-3

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