Abstract
This paper aims to detect car license plates in challenging scenarios such as distorted, high or low light, and dusty environments. This paper uses a deep learning network to extract information from a vehicle’s license plate efficiently. The Number Plate Identification System (NPIS) offers a wide range of applications in today’s digital world. Because of the fast growth of technology, the number of vehicles on the road is rapidly expanding. Even self-driving automobiles will be commonplace soon. This is causing a rise in the number of accidents and other tragedies that are both rapid and persistent. As a result, traffic and security monitoring are necessary. Previously, attributes were manually retrieved from an image/license plate, making the recognition process time-consuming and error-prone. This paper suggested a revolutionary machine learning strategy to identify license plate numbers in this study. A Convolutional Neural Network (CNN) is employed to automatically extract visual information, one of the most effective Deep Learning methods. The NPIS system de-noises and finds the edges present in the image. The generated picture is used for number recognition using the optical character recognition approach. These numbers are entered into the database to get the vehicle’s name, owner’s name, address, and mobile owner number.
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Saini, A., Kumar, K., Negi, A., Saini, P., Kashid, S. (2023). NPIS: Number Plate Identification System. In: Muthusamy, H., Botzheim, J., Nayak, R. (eds) Robotics, Control and Computer Vision. Lecture Notes in Electrical Engineering, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-99-0236-1_18
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DOI: https://doi.org/10.1007/978-981-99-0236-1_18
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