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A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System

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  • Pei-Fen Tsai

    (Institute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, Taiwan)

  • Jia-Yin Shiu

    (Institute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, Taiwan)

  • Shyan-Ming Yuan

    (Institute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, Taiwan)

Abstract

Recognizing low-resolution license plates from real-world scenes remains a challenging task. While deep learning-based super-resolution methods have been widely applied, most existing datasets rely on artificially degraded images, and common quality metrics poorly correlate with OCR accuracy. We construct a new paired low- and high-resolution license plate dataset from dashcam videos and propose a specialized super-resolution framework for license plate recognition. Only low-resolution images with OCR accuracy ≥5 are used to ensure sufficient feature information for effective perceptual learning. We analyze existing loss functions and introduce two novel perceptual losses—one CNN-based and one Transformer-based. Our approach improves recognition performance, achieving an average OCR accuracy of 85.14%.

Suggested Citation

  • Pei-Fen Tsai & Jia-Yin Shiu & Shyan-Ming Yuan, 2025. "A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System," Mathematics, MDPI, vol. 13(10), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1673-:d:1659915
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    1. Stefano Castriota & Mirco Tonin, 2023. "Stay or flee? Hit-and-run accidents, darkness and probability of punishment," European Journal of Law and Economics, Springer, vol. 55(1), pages 117-144, February.
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