Nanonets automates driver license OCR for North America's largest digital identity verification provider
The client processed over 50,000 ID documents per month using a traditional OCR engine supplemented by 10 manual-review contractors—an expensive, time-consuming, and error-prone arrangement that could not reliably handle the diversity of document formats or poor-quality photograph submissions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer submits ID document
A customer submits an ID document such as a driver's license or passport as part of the partner onboarding process.
Tools used
NanonetsAWS TextractAbbydocker containers
Outcome
Nanonets delivered a custom-trained OCR model with response times under 15 seconds that automatically validates image quality, extracts fields as JSON for direct application integration, and can run on-premises so user data never leaves the client's infrastructure.
What failed first
AWS Textract and Abby were both evaluated and found to provide insufficient accuracy; while they could extract data from some documents, they jumbled field values in many others and did not meet the client's automation threshold. Maintaining the traditional OCR solution internally was also ruled out as increasingly expensive with minimal accuracy improvement.