Abstract
This paper proposed a method based on Faster R-CNN algorithm to locate and recognize Chinese license plate. Faster R-CNN is composed by Region Proposal Network (RPN) and fast R-CNN. To make Faster R-CNN locate and recognize license plate more effective, we optimize the training process. To validate performance of the proposed method, two datasets (standard dataset and real scene dataset) are created. Faster R-CNN with three different model are used. The experimental results show that the proposed method achieve better performance contrasting six traditional methods. In standard dataset (simple situation), three modes achieve similar recognition results. However, in real scene dataset, more deeper model achieve better recognition performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ko, M.A., Kim, Y.M.: License plate surveillance system using weighted template matching. In: Applied Imagery Pattern Recognition Workshop, 2003, Proceedings (2003)
Jin-Jun, W.U., Shu-Xin, D.U.: Application of SVM to character recognition of vehicle license plate. J. Circuits Syst. 13(1), 84–87 (2008)
Nagare, A.P.: License plate character recognition system using neural network. Int. J. Comput. Appl. 25(10), 36–39 (2011)
Lécun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Science, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2016)
Uijlings, J.R., et al.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Simonyan, K., Zisserman, A.: very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Acknowledgement
This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF136).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Yang, Z., Du, FL., Xia, Y., Zheng, CH., Zhang, J. (2018). Automatic License Plate Recognition Based on Faster R-CNN Algorithm. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)