An efficient algorithm on vehicle license plate location
ABSTRACT This paper presents an efficient and smart approach to the vehicle license plate location based on characteristics of fractal dimension. The proposed algorithm consists of three major parts: pretreatment of license plate, search of license plate and extraction of plate region. In this study, we firstly research the normalization of license plate image and the criteria how to select the image enhancement method of license plate. Then the fractal dimension of license region and the interval of fractal dimension are all calculated. Finally the ultimate license plate region can be determined. The outstanding advantage of this algorithm is simple, smart and fast. Moreover it not only can be used to all kind of license plate in China but also has a good robustness under complex background, nonuniform illumination conditions and inclined license plate conditions. The performance of the proposed algorithm has been tested on a large number of experimental data from random and real images. Based on the experimental results, our algorithm shows both the missing rate and false detection rate are all zero. The possibility that the candidate region is more than one is 20%, meanwhile the probability of correction for inspect is 100%. The fact that this approach has superior performance in car license plate location is worth to note.
Conference Proceeding: A Fast Algorithm for License Plate Detection.[show abstract] [hide abstract]
ABSTRACT: In this paper we propose a method for detection of the car license plates in 2D gray images. In this method we first estimate the density of vertical edges in the image. The regions with high density vertical edges are good candidates for license plates. In order to filter out clutter regions possessing similar feature in the edge density image, we design a match filter which models the license plate pattern. By applying the proposed filter on the edge density image followed by a thresholding procedure, the locations of license plate candidates are detected. We finally extract the boundary of license plate(s) using the morphological operations. The result of experiments on car images (taken under different imaging conditions especially complex scenes) confirms the ability of the method for license plate detection. As the complexity of the proposed algorithm is low, it is considerably fast.Advances in Visual Information Systems, 9th International Conference, VISUAL 2007, Shanghai, China, June 28-29, 2007 Revised Selected Papers; 01/2007
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ABSTRACT: License-plate location in sensor images plays an important role in vehicle identification for automated transport systems (ATS). This paper presents a novel method based on vector quantization (VQ) to process vehicle images. The proposed method makes it possible to perform superior picture compression for archival purposes and to support effective location at the same time. As compared with classical approaches, VQ encoding can give some hints about the contents of image regions; such additional information can be exploited to boost location performance. The VQ system can be trained by way of examples; this gives the advantages of adaptiveness and on-field tuning. The approach has been tested in a real industrial application and included satisfactorily in a complete ATS for vehicle identificationIEEE Transactions on Industrial Electronics 03/2000; · 5.17 Impact Factor
Conference Proceeding: The recognition of car license plate for automatic parking system[show abstract] [hide abstract]
ABSTRACT: The recognition of a car's license plate for an automatic parking system is important for identifying the car at the entrance of the parking area because the car license plate has unique information for each car. This paper proposes the recognition of car license plate which is accurate and robust to environmental variation by using the car's license plate patterns according to motor vehicle regulation and a 4-layer BP neural network with supervised learning. In this method, the candidates regions of the car license plate are determined approximately according to the car license plate regulation such as color, the ratio and shape of the car license plate, the pattern of characters and numbers etc. For the results of recognition by neural networks, the candidate that has characters and numbers according to motor vehicle regulation is certified as license-plate region. Since the results of characters-pattern recognition are used to certify the license plate, the ability of license plate extraction is more accurate and the car can be identified simultaneously. The experimental results of seventy car images with the prototype of the automatic parking system show the performance of car license plate extraction rate of 96%, and the recognition rate is 92%Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium on; 02/1999