Abstract
License plate recognition is a very important component of intelligent parking lot management systems. The accuracy and speed of license plate recognition directly affect the speed of vehicles entering and leaving a parking lot. According to the characteristics of the character composition of a license plate, based on the convolutional neural network model LeNet-5, the license plate character recognition network model is designed. After training to the model, the recognition accuracy of each character in the license plate is higher than 99.97%, and the recognition duration of a single license plate is as fast as 1.31 ms. In this study, the detailed algorithm description of license plate recognition model is given; meanwhile, the optimization method for the neural network model is summarized.
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
Wang, W.: License plate recognition algorithm based on radial basis function neural networks. In: International Symposium on Intelligent Ubiquitous Computing & Education (2009)
Saha, S.: A review on automatic license plate recognition system (2019)
Li, L., Feng, G.: The license plate recognition system based on fuzzy theory and BP neural network. In: Fourth International Conference on Intelligent Computation Technology & Automation (2011)
Yao, Z., Yi, W.: Bionic vision system and its application in license plate recognition. Nat. Comput. 3, 1–11 (2019)
Jokic, A., Vukovic, N.: License plate recognition with compressive sensing based feature extraction (2019)
Iwasaki, A.: Independent randomness tests based on the orthogonalized non-overlapping template matching test (2019)
Raj, M.A.R., Abirami, S.: Structural representation-based off-line tamil handwritten character recognition. Soft Comput. 24(2), 1447–1472 (2019). https://doi.org/10.1007/s00500-019-03978-5
Redmon, J., et al.: You only look once: unified, real-time object detection (2015)
Wang, N., Zhu, X., Jian, Z.: License plate segmentation and recognition of chinese vehicle based on BPNN. In: International Conference on Computational Intelligence & Security. (2017)
Zhao, Z., Yang, S., Ma, Z.: License plate character recognition based on convolutional neural network LeNet-5. J. System Simul. 22(3), 638–641 (2010)
Melnyk, P., You, Z., Li, K.: A high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Comput. (2019). https://doi.org/10.1007/s00500-019-04083-3
Skaria, S., Al-Hourani, A., Lech, M., et al.: Hand-gesture recognition using two-antenna doppler radar with deep convolutional neural networks. IEEE Sens. J. 19(8), 3041–3048 (2019)
Quan, Z., Yang, W., Gao, G., et al.: Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web 22(2), 555–570 (2019)
Jin, R., Lu, L., Lee, J., et al.: Multi-representational convolutional neural networks for text classification. Comput. Intell. 35, 599–609 (2019)
Lecun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Acknowledgement
This work was supported by the Key Research and Development Project of Ganzhou, the name is “Research and Application of Key Technologies of License Plate Recognition and Parking Space Guidance in Intelligent Parking Lot”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tao, X., Li, L., Lu, L. (2020). A Lightweight Convolutional Neural Network for License Plate Character Recognition. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_28
Download citation
DOI: https://doi.org/10.1007/978-981-15-5577-0_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5576-3
Online ISBN: 978-981-15-5577-0
eBook Packages: Computer ScienceComputer Science (R0)