Academic Journal
A detailed review on license plate detection and recognition methods.
| Τίτλος: | A detailed review on license plate detection and recognition methods. |
|---|---|
| Συγγραφείς: | Li, Zhaokai, Ghaffar, Muhammad Arslan |
| Πηγή: | Journal of Traffic & Transportation Engineering (English Edition); Jun2026, Vol. 13 Issue 3, p974-1005, 32p |
| Θεματικοί όροι: | Automatic license plate readers, Intelligent transportation systems, Traffic monitoring, Autonomous vehicles, Mobile communication systems, Image segmentation, Machine learning |
| Περίληψη: | License plate detection and recognition (LPDR) is crucial for intelligent transportation systems (ITS) to ensure traffic safety and control. LPDR systems are widely used in traffic monitoring, vehicle safety, vehicle-to-vehicle (V2V) communication, and reducing traffic accidents. Modern technologies like autonomous driving and traffic optimization require secure V2V communication. Vehicles can use the LPDR system to identify nearby vehicles to communicate safely. With the expansion of applications in daily life, LPDR is facing many challenges. From the simple static camera used at the parking lot entrance, the LPDR systems are currently used for dynamic recognition of vehicle license plates (LPs), which is very difficult due to camera movement, camera angle, and distance from the vehicles. Recognizing LPs from different countries using a single algorithm is challenging since different nations use different characters, and LPs comprise multiple lines. For instance, Arabic characters could be more challenging to recognize. This article provides a performance comparison of several real-time tested and simulated LPDR methods. An ideal LPDR system must eliminate the challenges arising from new applications. The LPDR system needs to be designed to work accurately on static/dynamic conditions to develop V2V communication. By categorizing existing well-known LPDR approaches into conventional and machine learning techniques, this review tried to clarify the importance of each type of method. This work aims to review LP detection, character segmentation, and character recognition algorithms and provide guidance on future trends in this area. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Traffic & Transportation Engineering (English Edition) is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.jtte.2024.10.007? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: A detailed review on license plate detection and recognition methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Zhaokai%22">Li, Zhaokai</searchLink><br /><searchLink fieldCode="AR" term="%22Ghaffar%2C+Muhammad+Arslan%22">Ghaffar, Muhammad Arslan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Traffic & Transportation Engineering (English Edition); Jun2026, Vol. 13 Issue 3, p974-1005, 32p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+license+plate+readers%22">Automatic license plate readers</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+communication+systems%22">Mobile communication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: License plate detection and recognition (LPDR) is crucial for intelligent transportation systems (ITS) to ensure traffic safety and control. LPDR systems are widely used in traffic monitoring, vehicle safety, vehicle-to-vehicle (V2V) communication, and reducing traffic accidents. Modern technologies like autonomous driving and traffic optimization require secure V2V communication. Vehicles can use the LPDR system to identify nearby vehicles to communicate safely. With the expansion of applications in daily life, LPDR is facing many challenges. From the simple static camera used at the parking lot entrance, the LPDR systems are currently used for dynamic recognition of vehicle license plates (LPs), which is very difficult due to camera movement, camera angle, and distance from the vehicles. Recognizing LPs from different countries using a single algorithm is challenging since different nations use different characters, and LPs comprise multiple lines. For instance, Arabic characters could be more challenging to recognize. This article provides a performance comparison of several real-time tested and simulated LPDR methods. An ideal LPDR system must eliminate the challenges arising from new applications. The LPDR system needs to be designed to work accurately on static/dynamic conditions to develop V2V communication. By categorizing existing well-known LPDR approaches into conventional and machine learning techniques, this review tried to clarify the importance of each type of method. This work aims to review LP detection, character segmentation, and character recognition algorithms and provide guidance on future trends in this area. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Traffic & Transportation Engineering (English Edition) is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jtte.2024.10.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 974 Subjects: – SubjectFull: Automatic license plate readers Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Traffic monitoring Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Mobile communication systems Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A detailed review on license plate detection and recognition methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Zhaokai – PersonEntity: Name: NameFull: Ghaffar, Muhammad Arslan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20957564 Numbering: – Type: volume Value: 13 – Type: issue Value: 3 Titles: – TitleFull: Journal of Traffic & Transportation Engineering (English Edition) Type: main |
| ResultId | 1 |