This paper presents a convolutional neural network-based approach to identify the contents of low-quality images of license plates, with implications for criminal forensics investigations.
Forensic investigations often have to contend with extremely low-quality images that can provide critical evidence. Recent work has shown that, although not visually apparent, information can be recovered from such low-resolution and degraded images. The authors present a CNN-based approach to decipher the contents of low-quality images of license plates. Evaluation on synthetically-generated and real-world images, with resolutions ranging from 10 to 60 pixels in width and signal-to-noise ratios ranging from –3:0 to 20:0 dB, shows that the proposed approach can localize and extract content from severely degraded images, outperforming human performance and previous approaches. (Published Abstract Provided)