Nanonets automates passport and document data extraction for Dutch aviation HR firm
A Dutch aviation staffing company employed 60 data keyers to manually process 10,000 documents per month—passports, flight licenses, and resumes—at high cost, with low accuracy and significant inefficiency, compounded by varied image orientations, low-resolution scans, and multi-language content.
Traditional OCR tools including Amazon Textract, Abby, and Google Vision were tried but proved insufficient: they required extensive pre- and post-processing, could not handle multi-language documents or low-resolution images simultaneously, and did not support the client's custom data requirements.
The Nanonets OCR system fully automated document data entry with no human effort required, and the system learns over time so accuracy keeps improving.
Frequently asked questions
What did this team achieve with this AI workflow?
The Nanonets OCR system fully automated document data entry with no human effort required, and the system learns over time so accuracy keeps improving.
What tools did this team use?
Optical Character Recognition (OCR), docker.
What results were reported?
Manual data keyers employed: 60; Monthly documents processed manually: 10,000 documents a month; Human effort required after automation: No human effort was required; System accuracy trajectory: accuracy of the system keeps improving (source-reported, not independently verified).
What failed first in this deployment?
Traditional OCR tools including Amazon Textract, Abby, and Google Vision were tried but proved insufficient: they required extensive pre- and post-processing, could not handle multi-language documents or low-resolutio…
How is this data entry ops AI workflow structured?
Document upload trigger → Orientation detection and correction → Page and area identification → Multi-model OCR extraction → Database integration → Error-driven model retraining.