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Low Resolution License Plate Recognition Based on Intelligent Data Processing and Prediction Algorithm

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IoT and Big Data Technologies for Health Care (IoTCare 2021)

Abstract

Aiming at the problems of low accuracy of low resolution license plate recognition and long time consuming of recognition and registration in traditional methods, this paper proposes a low resolution license plate recognition method based on intelligent data processing and prediction algorithm. Firstly, the low resolution license plate is located, and the low resolution digital image of license plate is defined by the principle of image registration; Secondly, the doc scale space is constructed to determine the Gaussian pyramid and Gaussian difference pyramid model of low resolution license plate, and the RANSAC prediction algorithm is used to eliminate the mismatching of low resolution license plate and realize low resolution license plate recognition. The experimental results show that the proposed method can achieve fast recognition accuracy and recall rate of low resolution license plate, and the recognition time is low.

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Funding

1. Project name: incomplete license plate recognition study based on generative adversarial network, project number: KJQN201905503

2. Project name: The work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission, project number: KJZD-K201801901

3. Project name: research and design of intelligent manufacturing cloud platform system based on Internet of things, project number: KJQN201801902.

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Correspondence to Mi Meng .

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Meng, M., He, Ch. (2022). Low Resolution License Plate Recognition Based on Intelligent Data Processing and Prediction Algorithm. In: Wang, S., Zhang, Z., Xu, Y. (eds) IoT and Big Data Technologies for Health Care. IoTCare 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 414. Springer, Cham. https://doi.org/10.1007/978-3-030-94185-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-94185-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94184-0

  • Online ISBN: 978-3-030-94185-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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