Automated license plate system recognition for campus gate system
This project involves an license plate detection and character recognition system developed for the campus gate system. Commonly referred to as the Automated License Plate Recognition System (ALPR), it proves valuable in various scenarios, including monitoring vehicle entry and exit on university ca...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6487/1/fyp_CT_2024_LVC.pdf http://eprints.utar.edu.my/6487/ |
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| Summary: | This project involves an license plate detection and character recognition system developed for the campus gate system. Commonly referred to as the Automated License Plate Recognition System (ALPR), it proves valuable in various scenarios, including monitoring vehicle entry and exit on university campuses. Following the implementation of this project on campus, it successfully reduced the need for manpower, streamlined traffic flow control during peak hours, and prevented unregistered or uncategorized vehicles from entering the university premises. An ALPR system typically involves three fundamental actions: license plate detection, image processing, and license plate character’s recognition.
Primary objective of this project was to enhance recognition accuracy, angle checking, and handling of unconstrained scenes, oblique views, and more. This was achieved through a comprehensive evaluation of the developed ALPR system's performance. Given the remarkable advancements in machine learning today, this project proposes the utilization of YOLO, GAN methods and other tools for object detection, along with EasyOCR for optical character recognition. These technologies collectively extract characters and numbers from license plates to yield the final result.
The project primarily focuses on Malaysia's regional license plates, which adhere standards license plate format set by the Malaysia Road Transport Department (JPJ). However, some special cases may arise, such as logos positioned beside the license plate, or other factors that could potentially influence the accuracy rate of license plate detection and the availability of license plate character recognition. To facilitate this research, a dataset containing 1200 images of Malaysia's license plates was employed. This dataset encompasses challenging license plate images from various areas and acquisition states. |
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