Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network

Abstract

The present work is a study on the practical application of Learning process (Deep Learning) in the development of a system of Automatic recognition of vehicle license plates. These systems commonly referred to as ALPR (Automatic License Plate Recognition)-are able to recognize the content of vehicles from the images captured by a camera. The system proposed in this work is based on an image classifier developed through supervised learning techniques with convolution neural network. These networks are one of the most profound learning architectures and are specifically designed to solve artificial vision, such as pattern recognition and classification of images. This paper also examines basic processing techniques and Image segmentation-such as smoothing filters, contour detection-necessary for the proposed system to be able to extract the contents of the license plates for further analysis and classification. This paper demonstrates the feasibility of an ALPR system based on a convolution neural network, noting the critical importance it has to design a network architecture and training data set appropriate to the problem to be solved.