A cognitive and video-based approach for multinational License Plate Recognition
Identifieur interne : 000349 ( Main/Exploration ); précédent : 000348; suivant : 000350A cognitive and video-based approach for multinational License Plate Recognition
Auteurs : Nicolas Thome [France] ; Antoine Vacavant ; Lionel Robinault ; Serge Miguet [France]Source :
- Machine Vision and Applications [ 0932-8092 ] ; 2011-03.
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.
Url:
DOI: 10.1007/s00138-010-0246-3
Affiliations:
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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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li></region><settlement><li>Lyon</li></settlement><orgName><li>Université Claude Bernard Lyon 1</li><li>Université de Lyon</li></orgName></list><tree><noCountry><name sortKey="Robinault, Lionel" sort="Robinault, Lionel" uniqKey="Robinault L" first="Lionel" last="Robinault">Lionel Robinault</name><name sortKey="Vacavant, Antoine" sort="Vacavant, Antoine" uniqKey="Vacavant A" first="Antoine" last="Vacavant">Antoine Vacavant</name></noCountry><country name="France"><noRegion><name sortKey="Thome, Nicolas" sort="Thome, Nicolas" uniqKey="Thome N" first="Nicolas" last="Thome">Nicolas Thome</name></noRegion><name sortKey="Miguet, Serge" sort="Miguet, Serge" uniqKey="Miguet S" first="Serge" last="Miguet">Serge Miguet</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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