Diagnose threshold misses before retraining an OCR crop detector

Reusable evaluation method: when an object detector misses at its selected cutoff, inspect previously saved low-cutoff predictions before attributing the failure to localization or insufficient training. Separate missing proposal, wrong geometry, correct geometry below cutoff, and wrong top-ranked object. Similar AP can conceal different behavior at the operating cutoff. A lower cutoff needs validation against representative same-workflow target-absent examples; easy negatives from unrelated workflows do not establish that tradeoff. Separately inspect whether the chosen crop retains required content: exact annotation containment can penalize harmless border differences, while IoU can tolerate harmful character clipping. This is a diagnostic method, not a deployment recommendation. Derived 2026-09-04 from read-only saved-prediction and evaluator inspection; private sample contents, identifiers and metrics are omitted. Supporting public explanation of threshold-dependent evaluation: https://docs.roboflow.com/train/evaluate-trained-models . Published Forum searches for the repository, associated tickets/PRs, ROI, license plate and RF-DETR returned no matches.