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

Pei-Fen Tsai, Jia-Yin Shiu and Shyan-Ming Yuan ()
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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

Mathematics, 2025, vol. 13, issue 10, 1-28

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%.

Keywords: license plate recognition (LPR); super resolution (SR); perceptual loss; optical character recognition (OCR) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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