Abstract
Vehicle License Plate Detection and Recognition has become critical to traffic, security and surveillance applications. This contribution aims to implement and evaluate different techniques for License Plate Detection and Recognition in order to improve their accuracy. This work addresses various problems in detection such as adverse weather, illumination change and poor quality of captured images. After detecting the license plate location in an image the next challenge is to recognize each letter and digit. In this work three different approaches have been investigated to find which one performs best. Here, characters are classified through template matching, multi-class SVM, and convolutional neural network. The performance was measured empirically, with 36 classes each containing 400 images per class used for training and testing. For each algorithm empirical accuracy was assessed.
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References
Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: a survey. Trans. Intell. Transp. Sys. 9(3), 377–391 (2008)
Jia, W., Zhang, H., He, X.: Region-based license plate detection. J. Netw. Comput. Appl. 30(4), 1324–1333 (2007)
Roberts, L.G.: Machine perception of three-dimensional solids. Ph.D. thesis, Massachusetts Institute of Technology. Department of Electrical Engineering (1963)
Sandler, R., Vichik, S., Rosen, A.: License plate recognition - final report
Sandler, R., Vichik, S., Rosen, A.: Moving car license plate recognition - semesterial project, final report
Saha, S., Basu, S., Nasipuri, M.: License plate localization using vertical edge map and hough transform based technique, pp. 649–656. Springer, Heidelberg (2012)
Hongliang, B., Changping, L.: A hybrid license plate extraction method based on edge statistics and morphology, vol. 2, pp. 831–834 (2004)
Bai, H., Zhu, J., Liu, C.: A fast license plate extraction method on complex background. In: 2003 IEEE Intelligent Transportation Systems, Proceedings, vol. 2, pp. 985–987. IEEE (2003)
Wei, W., Wang, M., Huang, Z.: An automatic method of location for number-plate using color features. In: 2001 International Conference on Image Processing, Proceedings, vol. 1, pp. 782–785. IEEE (2001)
Kim, K.I., Jung, K., Kim, J.H.: Color texture-based object detection: an application to license plate localization. In: Pattern Recognition with Support Vector Machines, pp. 293–309. Springer (2002)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognit. 40(6), 1816–1824 (2007). 19 pp
Kim, K.I., Kim, K.K., Park, S.H., Jung, K., Park, M.H., Kim, H.J.: Vega Vision: a vision system for recognizing license plates (1999)
Ribaric, S., Adrinek, G., Segvic, S.: Real-time active visual tracking system, vol. 1, pp. 231–234, May 2004
Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, Hoboken (2009)
De Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: proceedings of the International Conference on Computer Vision Theory and Applications, Liston, Portugal, February 2009
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082 (2013)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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Rahman, M.J., Beauchemin, S.S., Bauer, M.A. (2020). License Plate Detection and Recognition: An Empirical Study. 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_24
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DOI: https://doi.org/10.1007/978-3-030-17795-9_24
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