A Systematic Review of Vehicle License Plate Recognition Algorithms Based on Image Segmentation


  • Aya Abdullateef Ezat Electronic Techniques Department, Al-Dour Technical Institute, Northern Technical University-Iraq.
  • Qusay Abboodi Ali Blended Learning Department, College of Administration and Economics, Tikrit University-Iraq.
  • Muhaned Al-Hashimi Department of Computer Science, College of Computer Science and Mathematics, Tikrit University-Iraq.




License plate detection, License plate Recognition, Segmentation, Threshold, VLPR


Recently, vehicle license plate recognition (VLPR) is a very significant topic in smart transportation. License plate (LP) has become an important and difficult research problem in recent years due to its difficulties such as detection speed, noise, effects, various lighting, and others. In the same context, most VLPR algorithms include should have many methods to be able to identify LP images based on different letters, colors, languages, complex backgrounds, distortions, hazardous situations, obstructions, vehicle speeds, vertical or horizontal lines, horizontal slopes, and lighting.  Therefore, this study provides a comprehensive review of current VLPR algorithms in the context of detection, and segmentation. Where, available VLPR algorithms are classified according to image segmentation methods (characteristics, and features) and are compared in terms of simplicity, complexity, uptime, problems, and obstacles.


Download data is not yet available.




How to Cite

Aya Abdullateef Ezat, Qusay Abboodi Ali, & Muhaned Al-Hashimi. (2023). A Systematic Review of Vehicle License Plate Recognition Algorithms Based on Image Segmentation. LC International Journal of STEM (ISSN: 2708-7123), 4(2), 25-34. https://doi.org/10.5281/zenodo.8239534