Abstract
License plate recognition has many applications in traffic systems. It is very difficult because images are usually noisy, broken or incomplete. In this paper, a novel robust approach for license plate recognition is proposed, which combines subspace projection with probabilistic neural network to improve the recognition rate. Probabilistic neural network is used as a classifier to identify low-dimension test samples which are obtained from actual license plate images by subspace projection. Experiment results show the effectiveness of the proposed method.
Research mainly supported by the NSFC (No. 60375004).
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© 2005 Springer-Verlag Berlin Heidelberg
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Hu, Y., Zhu, F., Zhang, X. (2005). A Novel Approach for License Plate Recognition Using Subspace Projection and Probabilistic Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_34
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DOI: https://doi.org/10.1007/11427445_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Singular Value Decomposition
- Recognition Rate
- Probabilistic Neural Network
- Signal Subspace
- Subspace Projection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.