Abstract
License plate recognition is done by recognizing the plate in single pictures. The license plate is analyzed in three steps namely the localization of the plate, the segmentation of the characters and the classification of the characters. Temporal redundant information has allready been used to improve the recognition rate, therefore fast algorithms have to be provided to get as many temporal classifications of a moving car as possible. In this paper a fast implementation for single classifications of license plates and performance increasing algorithms for statistical analysis other than a simple majority voting in image sequences are presented. The motivation of using the redundant information in image sequences and therefore classify one car multiple times is to have a more robust and converging classification where wrong single classifications can be suppressed.
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Zweng, A., Kampel, M. (2009). High Performance Implementation of License Plate Recognition in Image Sequences. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_57
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DOI: https://doi.org/10.1007/978-3-642-10520-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
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