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.
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.
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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.
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Frequently asked questions
What did this team achieve with this AI workflow?
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 use…
What tools did this team use?
Nanonets, AWS Textract, Abby, docker containers.
What results were reported?
Document processing response time: less than 15 seconds; Monthly documents processed: over 50,000; annual ID cards processed: over 10 million; Manual review contractors (prior state): 10 contractors (source-reported, not independently verified).
What failed first in this deployment?
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 automatio…
How is this kyc aml AI workflow structured?
Customer submits ID document → Automatic image quality validation → Custom model field extraction → JSON output for application integration.
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