Reliability Scoring for the Recognition of Degraded License Plates*

Abstract

Criminal investigations oftentimes need the identification of license plates of escape vehicles. The vehicles may be recorded by low-quality cameras in the wild. Their license plates may be unreadable for police officers. Recent efforts aim to use machine learning to forensically decipher license plates from such low-quality images. These methods operate near the information-theoretic limit of recognition and hence show quite high error rates. Unfortunately, it is unclear when such prediction errors occur, which makes it difficult to use these methods in practice. In this work, we propose a Bayesian Neural Network to inherently incorporate a reliability measure into the classifier. We additionally propose to integrate multiple estimations with an entropy weight to further improve the reliability. Our experiments show that this uncertainty metric dramatically reduces the number of false predictions while preserving most of the true predictions.