Abstract
The goal of an Automatic License Plate Recognition (ALPR) system is to capture and recognize a vehicle license plate. This is an important computer vision problem and has number of application domains: law enforcement, public safety agencies, and toll gate systems to name a few. At the heart of ALPR systems is the character recognition system as it is a unique identifier for any given vehicle. We construct an ALPR character recognition system by creating a dataset to simulate a captured license plate image, applying multiple binarization techniques to segment the characters from state, from the plate and from each other and finally using this dataset to train a convolutional neural network.
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Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3, pp. 958–962 (2003)
Weickert, J.: A review of nonlinear diffusion filtering. In: Scale-Space Theory in Computer Vision, pp. 1–28. Springer (1997)
Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)
Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circ. Syst. Video Technol. 23(2), 311–325 (2013)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Duffner, S.: Face image analysis with convolutional neural networks (2008)
Zhao, Z., Yang, S., Ma, X.: Chinese license plate recognition using a convolutional neural network. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, vol. 1, pp. 27–30. IEEE (2008)
Chen, Y.-N., Han, C.-C., Wang, C.-T., Jeng, B.-S., Fan, K.-C.: The application of a convolution neural network on face and license plate detection. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 552–555. IEEE (2006)
Han, C.-C., Hsieh, C.-T., Chen, Y.-N., Ho, G.-F., Fan, K.-C., Tsai, C.-L.: License plate detection and recognition using a dual-camera module in a large space. In: 2007 41st Annual IEEE International Carnahan Conference on Security Technology, pp. 307–312. IEEE (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). Similarity Matching in Computer Vision and Multimedia. http://www.sciencedirect.com/science/article/pii/S1077314207001555
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
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Angara, S., Robinson, M. (2020). License Plate Character Recognition Using Binarization and Convolutional Neural Networks. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_19
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DOI: https://doi.org/10.1007/978-3-030-17795-9_19
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