How Accurate Is OCR for Traffic and Parking Tickets?

There's no single honest accuracy percentage that applies to every ticket. Results depend on image quality, language, layout, fonts, handwriting, document damage, the specific field being extracted, and the OCR system doing the work. A useful evaluation measures field-level accuracy on representative documents — not whether the system extracted "some text" from a clean sample.

The fields where an error actually costs money

License plate, citation number, incident date and time, amount (including decimal placement), due date, and issuing agency — an error in any one of these can cause the wrong reservation match, the wrong amount billed, or a missed deadline. A misread street name matters far less than a misread plate.

Build a genuinely representative test set

Include clear PDFs, phone photos, shadows, rotation, folded notices, multi-page documents, multiple jurisdictions, handwriting, stamps, barcodes, and low-contrast printing — and remove or protect personal data during testing. Track exact-match accuracy for plates and citation IDs, normalized accuracy for dates and currency, the missing-field rate, the false-extraction rate, and the percentage of documents that end up needing manual correction. Don't publish an accuracy claim based on a handful of clean samples — that number won't hold up against a real inbox of scanned notices.

Use confidence as a routing signal, and preserve every correction

Low confidence should create a review task, not an automatic guess. High confidence should still get confirmation for any field that determines liability, a deadline, or an amount. Store the original extracted value, the corrected value, the reviewer, and the timestamp for every correction — the pattern in those corrections often reveals a recurring document layout that needs better handling, not just a one-off mistake.

Define acceptance rules, and be honest about where things stand

A document shouldn't proceed if the plate, incident date, citation number, or amount is missing or unverified — which fields are strictly required can vary by workflow and current Turo policy. Kelviz should publish a measured OCR performance number only after testing against a documented, representative dataset; until then, the ticket workflow is best described as a review aid, not a hands-off extraction guarantee.