INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
"NUMBER PLATE RECOGNITION USING OPENCV", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b36-b40, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404106.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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