Robust Korean License Plate Recognition Based on Deep Neural Networks
With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/12/4140 |
| _version_ | 1827689543937032192 |
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| author | Hanxiang Wang Yanfen Li L.-Minh Dang Hyeonjoon Moon |
| author_facet | Hanxiang Wang Yanfen Li L.-Minh Dang Hyeonjoon Moon |
| author_sort | Hanxiang Wang |
| collection | DOAJ |
| description | With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%. |
| first_indexed | 2024-03-10T10:21:07Z |
| format | Article |
| id | doaj.art-0e83e13aa655463f9d8e057f841c9ad4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:21:07Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0e83e13aa655463f9d8e057f841c9ad42023-11-22T00:23:35ZengMDPI AGSensors1424-82202021-06-012112414010.3390/s21124140Robust Korean License Plate Recognition Based on Deep Neural NetworksHanxiang Wang0Yanfen Li1L.-Minh Dang2Hyeonjoon Moon3Department of Computer Science and Engineering, Sejong University, Seoul 143-747(05006), KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 143-747(05006), KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 143-747(05006), KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 143-747(05006), KoreaWith the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%.https://www.mdpi.com/1424-8220/21/12/4140Korean license plate recognitionimage preprocessingdeep learning |
| spellingShingle | Hanxiang Wang Yanfen Li L.-Minh Dang Hyeonjoon Moon Robust Korean License Plate Recognition Based on Deep Neural Networks Sensors Korean license plate recognition image preprocessing deep learning |
| title | Robust Korean License Plate Recognition Based on Deep Neural Networks |
| title_full | Robust Korean License Plate Recognition Based on Deep Neural Networks |
| title_fullStr | Robust Korean License Plate Recognition Based on Deep Neural Networks |
| title_full_unstemmed | Robust Korean License Plate Recognition Based on Deep Neural Networks |
| title_short | Robust Korean License Plate Recognition Based on Deep Neural Networks |
| title_sort | robust korean license plate recognition based on deep neural networks |
| topic | Korean license plate recognition image preprocessing deep learning |
| url | https://www.mdpi.com/1424-8220/21/12/4140 |
| work_keys_str_mv | AT hanxiangwang robustkoreanlicenseplaterecognitionbasedondeepneuralnetworks AT yanfenli robustkoreanlicenseplaterecognitionbasedondeepneuralnetworks AT lminhdang robustkoreanlicenseplaterecognitionbasedondeepneuralnetworks AT hyeonjoonmoon robustkoreanlicenseplaterecognitionbasedondeepneuralnetworks |