data_entry_ops · services · workflow
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.
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 · Document upload trigger
The user uploads a PDF or image file to initiate the automated pipeline.
Tools used
Optical Character Recognition (OCR)docker
Outcome
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 failed first
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.
Results
Time saved10,000 documents a month
Volume60
Grounding & classification
Source type: vendor customer story
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computer visiondata extractiondocument aiidpocrid documentresumefailure mode describedtools describedworkflow describedprofessional servicesaccuracy improvementautomation rateemployee productivityvendor customer storydata entry opshr onboardinghr opsdocument to recordextract classify route