Automatic Number Plate Detection using Deep Learning

Automatic Number Plate Detection is an established method to interpret the letters in the number plates. In the last 5-10 years, the number of active vehicles has reached a tremendous growth, the growth has also resulted in increase of the illegal activities. It is hard to keep track of a vehicle due to rapid increase of the vehicles. It is crucially important to keep track of all vehicles by the belonging authorities. In this paper, we use technology open source platform called Tensor flow for machine learning. Primarily, the first step is to give the image of the car. Generally, the given image of the car is in low resolution and has satirical deficit in edge data. So, we need to process pictures which are present, it requires the high level precision. Secondly, this technology henceforth used to retrieve the pictures of the automobile, board which indicate it’s identify in the extracted picture also in a way cropped and converted into grayscale. Final output thus converted into grayscale so that the noise level of the image is reduced and the number plates of different colors also detected. So that the computer doesn’t need different algorithms for different colors. The letters of number plate in the image which is processed is extracted to text using optical character recognition. The extracted text is saved in Excel document, which can be used for future purposes. Assist more when compared with the cutting edge plate acknowledgment approach, the normal change is 3.6%. At long last, we propose a crossover chain of command classification framework relying somewhat using vector technique and the Bayesian rule-three methodology.