@ARTICLE{9310202,
  author={J. {Shashirangana} and H. {Padmasiri} and D. {Meedeniya} and C. {Perera}},
  journal={IEEE Access}, 
  title={Automated License Plate Recognition: A Survey on Methods and Techniques}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  abstract={With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions.},
  keywords={Licenses;Feature extraction;Optical character recognition software;Task analysis;Computer vision;Character recognition;Deep learning;Automatic license plate recognition (ALPR);character recognition;character segmentation;license plate detection;multi-stage plate recognition;single-stage plate recognition},
  doi={10.1109/ACCESS.2020.3047929},
  ISSN={2169-3536},
  month={},}