Automatic license plate recognition on microprocessors and custom computing platforms: A review
Abstract Automatic license plate recognition (ALPR) is the process of extracting and recognizing character information within a localized license plate region. Typically, ALPR involves three steps; image capture, image procession and plate recognition. The performance of an ALPR is largely dependent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-10-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12262 |
| _version_ | 1797991542262071296 |
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| author | Princewill Akpojotor Ph.D Adebayo Adetunmbi Boniface Alese Ayodeji Oluwatope |
| author_facet | Princewill Akpojotor Ph.D Adebayo Adetunmbi Boniface Alese Ayodeji Oluwatope |
| author_sort | Princewill Akpojotor Ph.D |
| collection | DOAJ |
| description | Abstract Automatic license plate recognition (ALPR) is the process of extracting and recognizing character information within a localized license plate region. Typically, ALPR involves three steps; image capture, image procession and plate recognition. The performance of an ALPR is largely dependent on the quality of the captured image, which is determined by factors such as environmental variation, camera quality and occlusion. Image procession and plate recognition step involves image processing techniques that extract and recognizes license plate and characters, respectively. ALPR systems could be realized on microprocessors (software‐based) or custom computing platforms (hardware‐based). Drawbacks such as portability, power consumption and computational speed limit software‐based ALPR for real‐time deployment. Custom platforms for ALPR consume less power and achieve high processing speed for real‐time capability. However, limited computing resources available within a custom chip make it difficult to implement State‐of‐the‐Art computationally intensive algorithms. Thus, very few literatures discussed ALPR techniques on custom computing platforms. This paper presents a comprehensive review of algorithms and architectures of ALPR on microprocessors and custom computing platforms. Design approaches, performance, gaps, suggestions and trends are discussed. |
| first_indexed | 2024-04-11T08:53:46Z |
| format | Article |
| id | doaj.art-4f5e568e09d9447ea03ff127539121cb |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T08:53:46Z |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-4f5e568e09d9447ea03ff127539121cb2022-12-22T04:33:21ZengWileyIET Image Processing1751-96591751-96672021-10-0115122717273510.1049/ipr2.12262Automatic license plate recognition on microprocessors and custom computing platforms: A reviewPrincewill Akpojotor Ph.D0Adebayo Adetunmbi1Boniface Alese2Ayodeji Oluwatope3Department of Computer Science Federal University of Technology Akure NigeriaDepartment of Computer Science Federal University of Technology Akure NigeriaDepartment of Computer Science Federal University of Technology Akure NigeriaDepartment of Computer Science and Engineering Obafemi Awolowo University Ile‐Ife NigeriaAbstract Automatic license plate recognition (ALPR) is the process of extracting and recognizing character information within a localized license plate region. Typically, ALPR involves three steps; image capture, image procession and plate recognition. The performance of an ALPR is largely dependent on the quality of the captured image, which is determined by factors such as environmental variation, camera quality and occlusion. Image procession and plate recognition step involves image processing techniques that extract and recognizes license plate and characters, respectively. ALPR systems could be realized on microprocessors (software‐based) or custom computing platforms (hardware‐based). Drawbacks such as portability, power consumption and computational speed limit software‐based ALPR for real‐time deployment. Custom platforms for ALPR consume less power and achieve high processing speed for real‐time capability. However, limited computing resources available within a custom chip make it difficult to implement State‐of‐the‐Art computationally intensive algorithms. Thus, very few literatures discussed ALPR techniques on custom computing platforms. This paper presents a comprehensive review of algorithms and architectures of ALPR on microprocessors and custom computing platforms. Design approaches, performance, gaps, suggestions and trends are discussed.https://doi.org/10.1049/ipr2.12262Image recognitionComputer vision and image processing techniquesTraffic engineering computing |
| spellingShingle | Princewill Akpojotor Ph.D Adebayo Adetunmbi Boniface Alese Ayodeji Oluwatope Automatic license plate recognition on microprocessors and custom computing platforms: A review IET Image Processing Image recognition Computer vision and image processing techniques Traffic engineering computing |
| title | Automatic license plate recognition on microprocessors and custom computing platforms: A review |
| title_full | Automatic license plate recognition on microprocessors and custom computing platforms: A review |
| title_fullStr | Automatic license plate recognition on microprocessors and custom computing platforms: A review |
| title_full_unstemmed | Automatic license plate recognition on microprocessors and custom computing platforms: A review |
| title_short | Automatic license plate recognition on microprocessors and custom computing platforms: A review |
| title_sort | automatic license plate recognition on microprocessors and custom computing platforms a review |
| topic | Image recognition Computer vision and image processing techniques Traffic engineering computing |
| url | https://doi.org/10.1049/ipr2.12262 |
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