A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System
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 con...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/10/1673 |
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| author | Pei-Fen Tsai Jia-Yin Shiu Shyan-Ming Yuan |
| author_facet | Pei-Fen Tsai Jia-Yin Shiu Shyan-Ming Yuan |
| author_sort | Pei-Fen Tsai |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-85d950c2dce44d03a10e08e89f349fd2 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-85d950c2dce44d03a10e08e89f349fd22025-08-20T01:56:20ZengMDPI AGMathematics2227-73902025-05-011310167310.3390/math13101673A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance SystemPei-Fen Tsai0Jia-Yin Shiu1Shyan-Ming Yuan2Institute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, TaiwanInstitute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, TaiwanInstitute of Computer Science and Engineering, Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu Campus, Hsinchu 30010, TaiwanRecognizing 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%.https://www.mdpi.com/2227-7390/13/10/1673license plate recognition (LPR)super resolution (SR)perceptual lossoptical character recognition (OCR) |
| spellingShingle | Pei-Fen Tsai Jia-Yin Shiu Shyan-Ming Yuan A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System Mathematics license plate recognition (LPR) super resolution (SR) perceptual loss optical character recognition (OCR) |
| title | A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System |
| title_full | A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System |
| title_fullStr | A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System |
| title_full_unstemmed | A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System |
| title_short | A Deep Learning Framework of Super Resolution for License Plate Recognition in Surveillance System |
| title_sort | deep learning framework of super resolution for license plate recognition in surveillance system |
| topic | license plate recognition (LPR) super resolution (SR) perceptual loss optical character recognition (OCR) |
| url | https://www.mdpi.com/2227-7390/13/10/1673 |
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