ICPR 2026 Competition on Low-Resolution License Plate Recognition
In surveillance contexts, license plate images are frequently captured at low resolutions or subjected to heavy compression due to storage and bandwidth limitations. Consequently, characters often become distorted, blend into the background, or overlap with neighboring symbols, making automated recognition particularly challenging.
Recognizing these low-resolution license plates remains a highly challenging and underexplored problem with strong forensic and societal relevance. The difficulty is evident in the fact that even state-of-the-art methods currently struggle to surpass 50-60% recognition accuracy. Improving recognition performance under such adverse conditions, therefore, offers a significant opportunity to substantially narrow investigative searches and expedite law enforcement decisions. This competition aims to encourage the development of advanced approaches, such as super-resolution, temporal modeling, and robust Optical Character Recognition (OCR) techniques, capable of operating effectively despite low-quality input conditions.