Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions
Over the past few years, breakthroughs in deep learning applied to computer vision has driven the development of novel techniques and various solutions for automatic license plate recognition (ALPR). Albeit this technology has been successfully used in constrained conditions, in-the-wild scenarios h...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11124903/ |
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| _version_ | 1849223077897961472 |
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| author | Amir Ismail Maroua Mehri Anis Sahbani Najoua Essoukri Ben Amara |
| author_facet | Amir Ismail Maroua Mehri Anis Sahbani Najoua Essoukri Ben Amara |
| author_sort | Amir Ismail |
| collection | DOAJ |
| description | Over the past few years, breakthroughs in deep learning applied to computer vision has driven the development of novel techniques and various solutions for automatic license plate recognition (ALPR). Albeit this technology has been successfully used in constrained conditions, in-the-wild scenarios have emerged as the new challenge for these systems. In-the-wild scenarios, also referred to as complex natural scenes or open scenarios, involve using regular video surveillance in various settings, including but not limited to drones and security robots. This survey is devoted to reviewing the state-of-the-art associated with in-the-wild ALPR. From a literature-based perspective, we (i) highlight specific approaches used for license plate (LP) detection, LP rectification, and LP recognition; (ii) present some of the most widely used datasets for benchmarking and summarized the results reported in the reviewed studies; and (iii) outline key research directions for advancing in-the-wild ALPR. This paper aims to provide practitioners, researchers, and experts with a comprehensive understanding of the current state of ALPR systems and the associated challenges, particularly in-the-wild scenarios. |
| format | Article |
| id | doaj-art-a76b387e23464cf5a18c94aa3099fa2a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a76b387e23464cf5a18c94aa3099fa2a2025-08-25T23:18:45ZengIEEEIEEE Access2169-35362025-01-011314538714541510.1109/ACCESS.2025.359897111124903Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future DirectionsAmir Ismail0https://orcid.org/0000-0003-0507-9566Maroua Mehri1https://orcid.org/0000-0002-4763-8584Anis Sahbani2Najoua Essoukri Ben Amara3LATIS-Laboratory of Advanced Technology and Intelligent Systems, Ecole Nationale d’Ingénieurs de Sousse, Université de Sousse, Sousse, TunisiaLATIS-Laboratory of Advanced Technology and Intelligent Systems, Ecole Nationale d’Ingénieurs de Sousse, Université de Sousse, Sousse, TunisiaEnova Robotics S.A., Sousse, TunisiaLATIS-Laboratory of Advanced Technology and Intelligent Systems, Ecole Nationale d’Ingénieurs de Sousse, Université de Sousse, Sousse, TunisiaOver the past few years, breakthroughs in deep learning applied to computer vision has driven the development of novel techniques and various solutions for automatic license plate recognition (ALPR). Albeit this technology has been successfully used in constrained conditions, in-the-wild scenarios have emerged as the new challenge for these systems. In-the-wild scenarios, also referred to as complex natural scenes or open scenarios, involve using regular video surveillance in various settings, including but not limited to drones and security robots. This survey is devoted to reviewing the state-of-the-art associated with in-the-wild ALPR. From a literature-based perspective, we (i) highlight specific approaches used for license plate (LP) detection, LP rectification, and LP recognition; (ii) present some of the most widely used datasets for benchmarking and summarized the results reported in the reviewed studies; and (iii) outline key research directions for advancing in-the-wild ALPR. This paper aims to provide practitioners, researchers, and experts with a comprehensive understanding of the current state of ALPR systems and the associated challenges, particularly in-the-wild scenarios.https://ieeexplore.ieee.org/document/11124903/License platedetectionrecognitiondeep learningin-the-wildsurvey |
| spellingShingle | Amir Ismail Maroua Mehri Anis Sahbani Najoua Essoukri Ben Amara Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions IEEE Access License plate detection recognition deep learning in-the-wild survey |
| title | Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions |
| title_full | Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions |
| title_fullStr | Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions |
| title_full_unstemmed | Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions |
| title_short | Automatic License Plate Recognition in In-the-Wild Scenarios: A Comprehensive Review, Open Issues, and Future Directions |
| title_sort | automatic license plate recognition in in the wild scenarios a comprehensive review open issues and future directions |
| topic | License plate detection recognition deep learning in-the-wild survey |
| url | https://ieeexplore.ieee.org/document/11124903/ |
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