Learning to Decipher License Plates in Severely Degraded Images

Abstract

License plate recognition is instrumental in many forensic investigations involving organized crime and gang crime, burglaries and trafficking of illicit goods or persons. After an incident, recordings are collected by police officers from cameras in-the-wild at gas stations or public facilities. In such an uncontrolled environment, a generally low image quality and strong compression oftentimes make it impossible to read license plates. Recent works showed that characters from US license plates can be reconstructed from noisy, low resolution pictures using convolutional neural networks (CNN). However, these studies do not involve compression, which is arguably the most prevalent image degradation in real investigations. In this paper, we present work toward closing this gap and investigate the impact of JPEG compression on license plate recognition from strongly degraded images. We show the efficacy of the CNN on a real-world dataset of Czech license plates. Using only synthetic data for training, we show that license plates with a width larger than 30 pixels, an SNR above –3 dB, and a JPEG quality factor down to 15 can at least partially be reconstructed. Additional analyses investigate the influence of the position of the character in the license plate and the similarity of characters.