Neural Network for Denoising and Reading Degraded License Plates

Abstract

The denoising and the interpretation of severely-degraded license plates is one of the main problems that law enforcement agencies face worldwide and everyday. In this paper, we present a system made by coupling two convolutional neural networks. The first one produces a denoised version of the input image; the second one takes the denoised and original images to estimate a prediction of each character in the plate. Considering the complexity of gathering training data for this task, we propose a way of creating and augmenting an artificial dataset, which also allows tailoring the training to the specific license plate format of a given country at little cost. The system is designed as a tool to aid law enforcement investigations when dealing with low resolution corrupted license plates. Compared to existing methods, our system provides both a denoised license plate and a prediction of the characters to enable a visual inspection and an accurate validation of the final result. We validated the system on a dataset of real license plates, yielding a sensible perceptual improvement and an average character classification accuracy of 93%.