ICPR 2026 Competition on Low-Resolution License Plate Recognition

TL;DR

The ICPR 2026 Competition on Low-Resolution License Plate Recognition aimed to advance LRLPR using real low-quality data, attracting 269 teams and highlighting current challenges and promising research directions.

Contribution

This paper introduces the first dedicated competition on LRLPR with a large dataset, detailed evaluation protocol, and analysis of top methods and future research trends.

Findings

Top team achieved 82.13% recognition rate

99 teams submitted valid entries, indicating high engagement

Current methods still face significant challenges in low-resolution scenarios

Abstract

Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and adverse imaging conditions can severely degrade license plate legibility. To promote progress in this area, we organized the ICPR 2026 Competition on Low-Resolution License Plate Recognition, the first competition specifically dedicated to LRLPR using real low-quality data collected under operationally relevant conditions. The competition was based on the LRLPR-26 dataset, which comprises 20,000 training tracks and 3,000 test tracks; each training track contains five low-resolution and five high-resolution images of the same license plate. Notably, a total of 269 teams from 41 countries registered for the competition, and 99 teams submitted valid entries in the Blind Test Phase. The winning team achieved a Recognition Rate of 82.13%, and four teams surpassed the 80% mark, highlighting both the high level of competition at the top of the leaderboard and the continued difficulty of the task. In addition to presenting the competition design, evaluation protocol, and main results, this paper summarizes the methods adopted by the top-5 teams and discusses current trends and promising directions for future research on LRLPR. The competition webpage is available at https://icpr26lrlpr.github.io/