ICPR 2026 Competition on Low-Resolution License Plate Recognition
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914504742273024 |
|---|---|
| author | Laroca, Rayson Nascimento, Valfride Kim, Donggun Chung, Sanghyeok Bae, Subin Seo, Uihwan Oh, Seungsang Phung, Chi M. Vo, Minh G. Ye, Xingsong Du, Yongkun Su, Yuchen Chen, Zhineng Heo, Sunhee Lee, Hyangwoo Na, Kihyun Nguyen, Khanh V. Vu Pham, Sang T. Phung, Duc N. N. Le, Trong P. Tran, Vy N. Vo Menotti, David |
| author_facet | Laroca, Rayson Nascimento, Valfride Kim, Donggun Chung, Sanghyeok Bae, Subin Seo, Uihwan Oh, Seungsang Phung, Chi M. Vo, Minh G. Ye, Xingsong Du, Yongkun Su, Yuchen Chen, Zhineng Heo, Sunhee Lee, Hyangwoo Na, Kihyun Nguyen, Khanh V. Vu Pham, Sang T. Phung, Duc N. N. Le, Trong P. Tran, Vy N. Vo Menotti, David |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22506 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ICPR 2026 Competition on Low-Resolution License Plate Recognition Laroca, Rayson Nascimento, Valfride Kim, Donggun Chung, Sanghyeok Bae, Subin Seo, Uihwan Oh, Seungsang Phung, Chi M. Vo, Minh G. Ye, Xingsong Du, Yongkun Su, Yuchen Chen, Zhineng Heo, Sunhee Lee, Hyangwoo Na, Kihyun Nguyen, Khanh V. Vu Pham, Sang T. Phung, Duc N. N. Le, Trong P. Tran, Vy N. Vo Menotti, David Computer Vision and Pattern Recognition 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/ |
| title | ICPR 2026 Competition on Low-Resolution License Plate Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.22506 |