NUMBER PLATE RECOGNITION USING OPENCV

Registration ID: IJNRD_217260

Published ID: IJNRD2404106

DOI: Click Here to Get

P. Vinay , G. Rajesh , K. Bhanu Prasad , B. Naveen Kumar , C. Vinay Sai

Keywords: Number Plate Recognition, OpenCV, Python, Optical Character Recognition (OCR), License Plate Detection, Image Processing, Computer Vision, k-Nearest Neighbors Algorithm (k-NN), Machine Learning, Pattern Recognition, Automatic Toll Collection, Traffic Management, Intelligent Traffic Systems.

With the popularization of automobile and the progress of computer vision detection technology, intelligent plate detection technology has gradually become an important part of intelligent traffic management. Plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and PYTHON under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license plate recognition. Number Plate Recognition (NPR) is a technology used to extract alphanumeric characters from images or video streams of vehicles' license plates. This process involves various steps such as image preprocessing, character segmentation, and optical character recognition (OCR). OpenCV (Open Source Computer Vision Library) provides a powerful platform for implementing ANPR systems due to its extensive collection of image processing functions and algorithms. In this paper, we present a comprehensive overview of ANPR using OpenCV, covering techniques for license plate detection, character segmentation, and OCR. We also discuss various challenges encountered in ANPR systems, such as variations in plate appearance, lighting conditions, and occlusions. Furthermore, we explore recent advancements in deep learning-based approaches for ANPR and discuss their potential for improving accuracy and robustness. Finally, we provide insights into future research directions and applications of ANPR technology in areas such as traffic management, law enforcement, and automated toll collection systems.

Published Paper   E-Certificate

P. Vinay, G. Rajesh, K. Bhanu Prasad, B. Naveen Kumar, & C. Vinay Sai (April-2024). NUMBER PLATE RECOGNITION USING OPENCV. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), b36-b40. https://ijnrd.org/papers/IJNRD2404106.pdf

Volume 9 Issue 4, April-2024

Pages : b36-b40

Paper Reg. ID: IJNRD_217260

Published Paper Id: IJNRD2404106

Downloads: 000121988

Research Area: Engineering

Country: Anantapur, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404106.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404106

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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