Driver's License Extraction
Fifty states, hundreds of designs, one task: read the license, verify the person.
Driver's license extraction is the automated reading of identity fields from license images: name, address, date of birth, license number, issue and expiry dates, class and restrictions — captured at onboarding, age verification, car rental, or any KYC checkpoint where the license serves as primary identity evidence. The document's difficulty is its diversity: in the US alone, each state issues its own designs and revises them on independent cycles, so production systems face hundreds of active layouts, plus international licenses for good measure — a template-per-format approach loses before it starts.
Extraction leans on the document's machine-readable ally where present: most modern North American licenses carry a PDF417 barcode encoding the identity fields in standardized (AAMVA) format, so the strongest pipelines read both — barcode for reliable data, visual OCR for the front — and compare them, because front/barcode mismatch is a first-order fraud signal (novelty and altered licenses routinely update the printed face and neglect or corrupt the barcode). Visual extraction itself must handle the security-feature clutter deliberately designed into licenses (guilloche patterns, ghost images, holographic overlays crossing the text), photographic capture conditions, and the field-label variation across jurisdictions. Validation stacks beyond format checks: expiry against the current date, license number patterns per issuing state, age arithmetic against date of birth.
In identity flows, extraction is one layer of a verification stack: authenticity checks (template conformance for the claimed state and version, security-feature presence, spoofing detection on the capture), biometric comparison of the portrait against a live selfie, and cross-checks against issuing-authority records where available. The data's sensitivity is regulated in its own right (driver's-license data carries specific statutory protection in many jurisdictions), which shapes retention, access, and — increasingly — where the extraction models themselves run.
Passports, national IDs, residence permits — thousands of formats, one job: read and verify the identity.
Two lines of OCR-B at the bottom of the page — the machine-readable zone that verification is built on.
Prove who you are before the account opens — the document-heavy front door of regulated finance.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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