Authors :
Vaibhav Gupta; Sudhanshu Shekhar Dadsena; M. Kameshwar Rao
Volume/Issue :
Volume 10 - 2025, Issue 6 - June
Google Scholar :
https://tinyurl.com/2p9mh726
DOI :
https://doi.org/10.38124/ijisrt/25jun057
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Automatic Number Plate Recognition (ANPR) is a computer vision technology that identifies vehicles by capturing
and interpreting license plate images. This paper presents a system built using Python, OpenCV, and machine learning
algorithms like Haar Cascade and Convolutional Neural Networks (CNN). The proposed model detects, segments, and
recognizes license plates in real-time from traffic images or video. This system is highly useful for smart parking systems,
traffic monitoring, toll collection, and security surveillance. The project integrates image preprocessing, plate localization,
character segmentation, and optical character recognition (OCR) to achieve accurate results. Future improvements may
include real-time deployment, integration with vehicle databases, and alert systems for stolen vehicles.
Keywords :
ANPR, Open CV, License Plate Detection, OCR, CNN, Haar Cascade, Smart Surveillance.
References :
- GitHub Repo: https://github.com/AarohiSingla/Automatic-Number-Plate-Recognition--ANPR
- OpenCV Documentation - https://docs.opencv.org/
- Smith, R. (2007). An overview of the Tesseract OCR engine.
- YOLOv5 License Plate Detection Research Papers
- Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2013). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions
- OpenALPR Project - https://www.openalpr.com/
- Python-Tesseract Documentation - https://pypi.org/project/pytesseract/
Automatic Number Plate Recognition (ANPR) is a computer vision technology that identifies vehicles by capturing
and interpreting license plate images. This paper presents a system built using Python, OpenCV, and machine learning
algorithms like Haar Cascade and Convolutional Neural Networks (CNN). The proposed model detects, segments, and
recognizes license plates in real-time from traffic images or video. This system is highly useful for smart parking systems,
traffic monitoring, toll collection, and security surveillance. The project integrates image preprocessing, plate localization,
character segmentation, and optical character recognition (OCR) to achieve accurate results. Future improvements may
include real-time deployment, integration with vehicle databases, and alert systems for stolen vehicles.
Keywords :
ANPR, Open CV, License Plate Detection, OCR, CNN, Haar Cascade, Smart Surveillance.
