EFFECTIVE PLATE OBJECT SEGMENTATION AND RECOGNITION BY IMPLEMENTING ARTIFICIAL INTELLIGENT MODEL FOR SMART TRANSPORTATION SYSTEM.

Abstract

The increase in the number of vehicles in the last few years has made it challenging to manually note the number plate text of the vehicle. Hence, in order to reduce the manual work, there is a need to propose a methodology that can detect the number plate region from the input image and recognize the characters of the number plate. Systems have been built for the same using Image Processing techniques, but this technique fails to provide accurate results occasionally in the case of real data. Modern technology such as Deep Learning overcomes this problem. Hence, a deep learning-based methodology is proposed to detect the number plate region from the input image and recognize its characters. Using the pre-trained based deep learning Networks. the number plate region is detected and using Convolutional Neural Networks (CNN), the characters are recognized from the detected plate region. The system also stores the number plate text with its state name into the database to maintain a record of number plates detected. The proposed system provides promising results. The model is judged against other models that are already out there, such as ResNet50, DenseNet, and DenseCapsNet. According to these studies, MobileNet has reached a level of accuracy of 98.69%, which is higher than other algorithms that are thought to be cutting-edge.