International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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A Review on Automatic Number Plate Recognition Adeen Shaikh1, Dr. Pallavi Tawde2 1 Student, Department of MSc. IT, Nagindas Khandwala College, Mumbai, Maharashtra, India 2 Assistant professor ,Department of IT and CS, Nagindas Khandwala College, Mumbai, Maharashtra, India
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Abstract - Automated Number Plate Recognition is an
the plate is extracted. The final segmentation stage of the neural network involves employing a picture segmentation approach, which includes mathematical morphology, color analysis, and histogram analysis. Segmentation, a character recognition technique, is applied to each identified character. Optical Character Recognition (OCR) is utilized as one of the methods to recognize individual characters, utilizing a preserved database for each alphanumeric character.
essential tool used in modern traffic management systems. Implementing image processing techniques, this technology performs an automatic detection of characters on a vehicle’s license plate . In such a way, ANPR contributes to traffic monitoring, provides for enhanced security, and facilitates the enforcement of applicable traffic rules. Particular ANPR systems are indispensable for efficient traffic control and surveillance, which includes criminal investigation, toll collection, speed management, parking control and many other tasks. Thanks to the advances made in the field of image processing algorithms, ANPR systems can now handle the task of accurately generating and sorting tags of license plates, which is due to providing for the development of intelligent traffic management systems.
The VLPR framework facilitates various traffic applications and enhances safety measures, encompassing parking lot surveillance, automated toll collection, road traffic monitoring, vehicle enforcement, traffic volume analysis, and crime prevention. It enables the implementation of traffic control measures. Through frame sequences or still images, this algorithm accurately identifies license plate numbers. Automatic automobile plate recognition (APR) utilizes image processing, object identification, and pattern recognition coupled with optical character recognition (OCR) to detect license plates.
Key Words: ANPR, Automated Number Plate Recognition, Modern traffic management systems, Image processing techniques, Automatic detection.
1.INTRODUCTION
2. LITERATURE REVIEW
In various contexts, vehicle platform detection and identification are utilized, encompassing tasks such as estimating travel times, conducting traffic surveys, detecting violations, and enhancing surveillance systems. The rising population also contributes significantly to the proliferation of automobiles. Recently, many students and faculty members at educational institutions have encountered challenges in finding parking spaces. Due to the lack of security personnel capable of maintaining vehicle records manually, most parking lots are operated by security guards. Consequently, drivers often have to navigate parking lots on foot to secure a spot, which can lead to vehicle thefts and conflicts among drivers vying for parking spaces. Automated License Plate Recognition (ALPR), also known as ANPR, represents a breakthrough in image processing technology utilized for vehicle identification. The integration of ALPR technology into security and traffic management systems is a forward-looking endeavor. The Tag Reconnaissance System exemplifies a computer vision application designed to extract pertinent information from digital images, such as license plate size and contours, which aids in distinguishing between multiple tags.
Saqib Rasheed et. al (2012) The research article presented an “Automated Number Plate Recognition system using Hough lines and template matching” to develop a viable way of identification and recognition of license plates. This system was built on the principle of Hough Transformation and a template matching model. The detection of plates was done through the use of Canny detector and Hough transformation while the identification of license numbers was done through matching templates. The system was successful in extracting vehicle plate and identified 94.11% of vehicles . The ANPR system produced a success rate of 89.70% after standardization of number plates for Islamabad. Ravi Kiran Varma et. all (2019) The purpose of the paper was to identify Indian vehicle license plates. Throughout the work, the authors illustrated the progress in terms of preprocessing and providing a scheme that resolves numerous issues, such as varying lighting conditions of images, hazy or skewed images, and background noise. The significance of the study is associated with the use of top-level image processing techniques during the pre-processing run, including morphological transformations and Gaussian filtering, which improve the identification of both standard and partially worn-out numeric plates in wider scenarios.
To utilize the ANPR system, it's necessary to place your vehicle appropriately and capture either a frontal or rear image. Subsequently, the license plate number is located and
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