• Vanne Nandini
• Ch. Satyananda Reddy

An exponential growth in many vehicles necessitates the use of automated schemes to preserve vehicle information. The evidence is extremely required for both management of traffic as well as reduction of crime. Number plate recognition is an actual way of automatic vehicle identification. Some of the current algorithms based on the standard of learning take a lot of time and proficiency before delivering satisfactory results but even then, lacks accuracy. In the planned algorithm an effective technique for recognition of vehicle number plates has been devised. This project presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Various contemporary procedures for car license plate detection are reasonably effective under specific circumstances or strong assumptions only. Though, they exhibit poor presentation when the evaluated car license plate images have a degree of revolution, as a result of manual detention by traffic police or deviation of the camera. Therefore, we propose the CNN-Model, ImageNet, VGG-16, and RNN framework and for more accuracy, we used Faster R-CNN and CRNN for car license plate detection. Using perfect rotation angle estimates and a fast intersection-over-union valuation strategy, our projected approach can elegantly manage rotational problems in actual-time circumstances. The algorithm goals at addressing the difficulties of scaling and recognizing the situation of characters with a decent accuracy rate

Deep Learning, Vehicle Detection, Convolutional Neural Network, Pattern Recognition

"END-TO-END CAR LICENCE PLATE DETECTION AND RECOGNITION WITH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.d269-d277, August-2022, Available :http://www.jetir.org/papers/JETIR2208334.pdf

"END-TO-END CAR LICENCE PLATE DETECTION AND RECOGNITION WITH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppd269-d277, August-2022, Available at : http://www.jetir.org/papers/JETIR2208334.pdf