Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model

Deep learning is playing a vital role in most of the applications being used in today's technological era. There seems to be no application where the use of deep learning concepts has not been used yet. One such application is the detection of number plates of vehicles on roads. The incidence of automobile crashes has greatly expanded along with the population growth. Their needs to be system that could automatically detect the vehicle's number plate whenever necessary. The situation arises when any of the rider does not obey to the traffic rules, or skips the traffic signals or indulges in any other sort of activities such as accidents. In these situations, the rider tries to run away. In order to detect the person, these kind of deep learning models helps us to detect the vehicle. Image labelers on the roadsides. Need of sensors are required to store the data and they need to be deployed on the roadside. These labelers are used to track the vehicles that are not obeying the traffic rules. With the use of sensors, it could be stated that the use of IoT also plays in the deployment of the current work when it comes to real-time. The model initially is trained with numerous number plates. The number plates have various patterns of texts and numbers and it is essential for the deep learning model to get trained. With successful training, the model is able to detect the new number plates. The model proposed in the current paper is compared with the other existing systems and proves to be efficient and cost-efficient than them.