License Plate Detection and Recognition by Convolutional Neural Networks

Abstract

The current advancements in machine intelligence have expedited the process of recognizing vehicles and other objects on the roads. Several methods including Deep Learning techniques have been proposed recently for LPR, yet those methods are limited to specific regions or privately collected datasets. In this paper, we propose an end-to-end Deep Convolutional Neural Network system for license plate recognition that is not limited to a specific region or country. We apply a modified version of YOLO v2 to first recognize the vehicle and then locate the license plate. Moreover, through the convolutional procedures, we improve an Optical Character Recognition network (OCR-Net) to recognize the license plate numbers and letters. Our method performs well for different vehicle types. Our system overcomes tilted and distorted license plate images and performs adequately under various illumination conditions, and noisy backgrounds. Our experimental results on 4,837 images of stationary and moving vehicles (cars, buses, motorbikes, and trucks) from different countries show that our proposed system achieved recognition rates between 88.5% and 98.04%, outperforming the state-of-the-art commercial and academic methods for challenging images.