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%.

Neural Network for Denoising and Reading Degraded License Plates

Milani S.
2021

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%.
2021
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-3-030-68779-3
978-3-030-68780-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3392247
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