Automatic License plate / Number plate / Registration plate recognition is recognized as an automation which evolved mostly based on image processing techniques. It has been extensively used in recognizing vehicles in applications such as red-light enforcement, over speeding, parking control, toll collection. The main objective of the paper is to identify the most well-planned way to identify the registration plate from the digital image (gained from the camera) and recognize with high accuracy. ANPR is employed to localize the license plates, segment each character and extract the text from the license plate and then recognition each character successfully. The main issue of registration plate recognition rely on the accuracy rate. Advancement in deep learning methods has improved the ability to solve visual recognition task. Henceforth using deep Convolutional Neural Networks (DCNN) will intensification the precision, recall, processing speed, reduce the error rate in solving the ANPR process. The use of deep learning CNN helps in identification of license plates of any aspect ratio which would work good for places like India where license plate style differs remarkably. The CNNs are up skilled and balanced so that they are strong under various states like variations in pose, lighting, occlusion etc. In our dataset we have used 100 images to train our network and obtained an 99% accuracy for plate localization and 93% accuracy for recognition.
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Dheeba J
Dheeba J
School of Computer Science and Engineering, Vellore Institute of Technology,
Vellore, Tamil Nadu, India.
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Cite this article
Mahalakshmi S and Dheeba J, “Robust Approach of Automatic Number Plate
Recognition System using Deep CNN”, Journal of Machine and Computing, pp. 853-860, October 2024.
doi:10.53759/7669/jmc202404079.
