Comparison of ML algorithms for identification of Automated Number Plate Recognition

Bhardwaj, Dinesh; Kaur, Harjinder

doi:10.1109/icrito.2014.7014770

Cited by 5 publications

(1 citation statement)

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“…In, 9 Red, Green, and Blue channels are used by the authors to divide the license plate identification issue. Each channel's image serves as the input for CNN, this learns the topological characteristics using the data.…”

Section: Literature Reviewmentioning

confidence: 99%

License plate recognition using machine learning

Rahman,

Merag,

Muhtasim

et al. 2023

International Conference on Images, Signals, and Computing (ICISC 2023)

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Car owners altering license plates using different typefaces and designs violate the law that strictly forbids such behaviour. Traffic police officers claim that changing the license plates makes it impossible to read the registration numbers due to an increase in fatal street collisions and car thefts. They worry that it may be difficult to track down vehicles used in hit-and-run incidents. It is challenging to impose further limitations on any algorithm used to identify and recognise license plates in a developing nation like Bangladesh. This work has the primary objective of designing a reliable detection and recognition system for transitional, standard car license plates, which are frequently seen in developing countries. Increase the effectiveness of reading license plates drawn or printed in various styles and typefaces employing cuttingedge technology, including machine learning (ML) models. For this study, You Only Look Once (YOLOv3) is used to utilising the most recent version of the object detection method. The raw image is pre-processed to increase its quality and then divided into appropriate-sized grid cells to determine where the license plate should be placed. After that, the data is post-processed, and the accuracy of the proposed model is evaluted using industry-recognised standards. A sizeable image dataset was used to be tested using this proposed methodology. The presented system is expected to be essential for vehicle monitoring, parking fee collection, lowering traffic accidents, and identifying unregistered vehicles. The results demonstrate that the suggested method achieves 97.1% mAP, 95.3% precision and 96.8% in plate detection.

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Section: Literature Reviewmentioning

confidence: 99%