Automated Gate System Using Number Plate Recognition (NPR)

Anekar, Shreya; Yeginwar, Samrudhi; Sonune, Harsha

doi:10.1007/978-981-16-5987-4_42

Cited by 2 publications

(1 citation statement)

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“…It primarily serves to strengthen the overall safety and security of the system. In addition to being employed at entrance points to different workplaces, societies, and other establishments, the vehicle license plate (LP) recognition system may also be used for vehicle authentication [1], [2]. This technique has the potential to be completely automated.…”

Section: Introductionmentioning

confidence: 99%

Character level vehicle license detection using multi layered feed forward back propagation neural network

Ummadisetti

Thiruvengatanadhan²,

Narayana³

et al. 2023

Bulletin EEI

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Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

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Section: Introductionmentioning

confidence: 99%