License Plate Detection Applying Contours

Abstract

A vehicle is a machine that can convey people and goods from a source to a destination. When vehicles ply on roads, it can lead to traffic. Traffic has to be managed on a day to day basis in villages, towns, cities and the freeway. A vehicle license plate is affixed to the vehicle at the time of registration or at some other possible event. A vehicle license plate can help in identification, management and routing of vehicles. Lately, high security registration plates (HSRP) have been assigned in India. Vehicle license plate localization is a well-known and well-studied problem. By localizing the license plate on a vehicle, it is possible to get the image of the license plate. In this research article, a new algorithm – license plate localization applying contours (LILY) is presented.

Introduction

The document discusses vehicle license plate detection and recognition, highlighting its importance for vehicle identification, management, parking, and maintenance. License plate detection is a well-studied problem typically addressed using image processing and machine learning techniques such as connected component analysis, morphological operations, and edge detection.

In India, High Security Registration Plates have been introduced for better vehicle security. A typical license plate detection system includes a camera for image capture, a personal computer for processing, and application software for detection.

The text reviews multiple research works employing various algorithms and technologies for license plate recognition (LPR), including SqueezeNet, YOLO versions, CNNs, Mask R-CNN, Hopfield Neural Networks, and HSV color space methods. Common steps across these methods involve image acquisition, pre-processing (grayscale conversion, noise removal, filtering), license plate localization/detection, character segmentation, and character recognition using techniques like template matching, OCR, or neural networks.

Several algorithms incorporate advanced image processing techniques such as adaptive thresholding, contour detection, morphological operations, and machine learning for feature extraction and classification. Some systems focus on robustness under environmental challenges like varying light, weather conditions, and tilted plates.

The document concludes with the description of a new methodology called the LILY algorithm, which preprocesses images, applies Canny edge detection, extracts contours, and identifies quadrilateral shapes as license plates. Results demonstrate successful detection on sample images.

Conclusion

License plate detection has been a well-researched problem. This research article presents a thorough review of the literature in this subject. A new algorithm called LILY (License plate detection applying contours) is presented. The LILY algorithm has satisfactory results.

References

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Copyright

Copyright © 2025 Bhumika Yadav, Dr. Ashish Tamrakar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.