HAT Super-Resolution and a PARSeq+CLIP4STR Voting Ensemble for Extreme In-the-Wild License Plate Recognition

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[Submitted on 9 Jul 2026]

Title:HAT Super-Resolution and a PARSeq+CLIP4STR Voting Ensemble for Extreme In-the-Wild License Plate Recognition

View a PDF of the paper titled HAT Super-Resolution and a PARSeq+CLIP4STR Voting Ensemble for Extreme In-the-Wild License Plate Recognition, by Karthik Sivarama Krishnan and 1 other authors

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Abstract:We describe our entry to the ICIP 2026 Grand Challenge on Extreme In-the-Wild License Plate Super-Resolution (XLPSR), which scored 9.73 wECR on the public validation leaderboard. The system pairs a Hybrid Attention Transformer super-resolution (HAT) front-end with an ensemble of two scene-text recognisers (PARSeq-S and CLIP4STR-B) and a confidence-weighted character-voting scheme that abstains on uncertain positions. We treat XLPSR as a recognition task gated by image legibility: the SR step exists to lift characters out of sub-pixel territory, and the asymmetric scoring rule (+2 / -1 / 0) is exploited explicitly through abstention. Our pipeline runs in 1.7 s per sequence on RTX 3090 (max 2.7 s, p99 2.4 s), well under the 60 s/sequence Docker budget.

Comments: 2 pages, 1 figure, 1 table. Accepted at the IEEE ICIP 2026 Grand Challenge on Extreme In-the-Wild License Plate Super-Resolution (XLPSR). Top-8 finalist

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.08896 [cs.CV]

(or arXiv:2607.08896v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.08896

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Karthik Sivarama Krishnan [view email] [v1] Thu, 9 Jul 2026 19:36:57 UTC (6 KB)

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