License Plate Character Recognition Using Binarization and Convolutional Neural Networks

Abstract

The goal of an Automatic License Plate Recognition (ALPR) system is to capture and recognize a vehicle license plate. This is an important computer vision problem and has number of application domains: law enforcement, public safety agencies, and toll gate systems to name a few. At the heart of ALPR systems is the character recognition system as it is a unique identifier for any given vehicle. We construct an ALPR character recognition system by creating a dataset to simulate a captured license plate image, applying multiple binarization techniques to segment the characters from state, from the plate and from each other and finally using this dataset to train a convolutional neural network.