Improved License Plate Recognition for Low-Resolution CCTV Forensics by Integrating Sparse Representation-Based Super-Resolution

摘要

Automatic license plate recognition (LPR) is an important functionality for closed-circuit television (CCTV) forensics. However, uncontrolled capture conditions make it still difficult to achieve effective LPR in practice. In this paper, we propose a novel method for robust LPR in real-world imagery, leveraging sparse representation-based (SR-based) super-resolution. To that end, we make use of high-resolution license plate (LP) images that are used for both (1) the construction of a dictionary for SR-based super-resolution and (2) the training of LP character classifiers. Comparative experimental results indicate that the proposed SR-based super-resolution method allows for effective LPR in low-resolution imagery captured by long-distance CCTV cameras.