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License Plate Recognition Model Based on CNN+LSTM+CTC

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

Abstract

With the continuous improvement of the social and economic level, the number of vehicles has exploded in the city, and traditional manual identification license plates have been unable to meet the demand. In this paper, a Convolutional neural network (CNN)-based license plate recognition system is designed. The recognition module uses the CNN+LSTM+CTC model to simplify the convolutional layer structure to adapt to the lightweight training mode. The two-way LSTM structure is used to learn from both sides of the license plate to enhance the end-to-end recognition effect. Compared with the traditional scheme, the CTC loss calculation method eliminates the need for character alignment, streamlines the steps, and improves the recognition accuracy. The experiment shows that the license plate recognition software system designed in this paper has a high recognition accuracy rate of 98.59%.

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Author information

Authors and Affiliations

  1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130062, China

    Hangming Zhang, Feng Sun & Xiaopu Zhang

  2. The State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130061, China

    Lingling Zheng

Authors
  1. Hangming Zhang
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  2. Feng Sun
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  3. Xiaopu Zhang
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  4. Lingling Zheng
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Corresponding author

Correspondence to Lingling Zheng .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2019 Springer Nature Singapore Pte Ltd.

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Zhang, H., Sun, F., Zhang, X., Zheng, L. (2019). License Plate Recognition Model Based on CNN+LSTM+CTC. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_52

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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