Accuracy claims are only as good as the methodology behind them. Every figure on the License Plate OCR page comes from a single public, reproducible benchmark run — here is exactly how it was measured.
We evaluate on the
OpenALPR public US benchmark
— 222 real-world images of US plates in the wild: highways, parking lots, varied angles,
lighting and distances. It is the industry-standard set commercial ALPR vendors quote,
which makes our numbers directly comparable. The run was measured in
July 2026 with
no location hints: every image was scored
cold, with no state_suggestions
prior — the hardest configuration, and the one your accuracy can only improve on.
The letter O and the digit 0 are scored as equivalent, the standard industry convention — they are visually identical on most US plate dies, and many DMVs treat them as interchangeable. Scored strictly, with O and 0 counted as distinct characters, exact plate reads are 95.0%. Both figures are reported by the evaluation harness on every run.
Two of the benchmark's 222 labels are wrong. After manual inspection of the original images, we corrected them — and rather than silently editing the benchmark, the corrections are published alongside our evaluation harness so any run is reproducible and auditable:
The benchmark is used strictly for evaluation. Its images are excluded from all training data, so these figures measure how the system generalizes to plates it has never seen — not how well it memorized a test set.