Extracting data from forms, certificates and identity documents is one of the most practical applications of machine learning. Modern OCR and layout models read structured and semi-structured documents remarkably well. But “remarkably well” is not the same as “always right” — and in operational settings the cost of a wrong field can be significant.
Confidence is a design input
Every extracted field should carry a confidence score, and every workflow should define what happens at each level: accept automatically, accept with sampling, or route for human review. Thresholds are set per field, because a misread reference number and a misread date of birth do not carry the same risk.
Validate against the world, not just the model
Cross-checks are often worth more than a better model. Check digits, date logic, reference data and consistency between fields catch errors that confidence scores miss. Combining model output with deterministic validation is what turns extraction into something an organisation can rely on.
Design the review experience
When a person reviews a result, they should see the source region alongside the extracted value, understand why it was flagged and correct it in seconds. Corrections become labelled data for measuring — and improving — performance over time.
Measure before you automate
We build an evaluation set before tuning anything: representative documents, ground truth agreed with the client and metrics for each field. Development and demonstrations use synthetic, non-identifying documents; production data stays in the environment the client approves.
Automation should remove effort, not responsibility.
Where documents inform decisions about people, rights or compliance, the system’s job is to prepare and check the evidence. The decision — and the accountability for it — stays with a person.