Computer vision systems that anchor ID trust in MRZ check digits rather than OCR, read paper exam forms, and recognise faces without storing photos.
Reading an Image Is Easy. Knowing the Data Is Right Is Not.
Data still lives on paper, in photos and on cards. We don't stop at reading it; we verify it. On ID cards we anchor trust in the MRZ check digits on the back, not in front-side OCR; on paper exam forms we align the image with registration marks and send ambiguous marks to a person instead of guessing.
When the system cannot be sure, it reports instead of guessing. Privacy is part of the design: we match faces as mathematical signatures, not photos, and we develop models on synthetic data we generate rather than real personal data. We measure accuracy on real documents, not on demo data.
What We Build
- Identity document extraction and ICAO 9303 MRZ validation
- Face matching, liveness and anti-spoofing checks
- Optical exam form reading from DAT files, PDFs and images, with registration-mark alignment
- Product and document recognition by QR code and barcode
- Document and certificate verification portals
- Training on synthetic data and accuracy measured on real documents
Where It Fits
Remote customer onboarding (KYC), paper exam scoring, certificate verification, accounting and paperwork automation, personalised greetings.
In Our Own Projects
UNITICID: our identity verification pipeline built on mathematics. An optical reader for paper exam forms and a certificate verification portal, built for the Yunus Emre Institute. Face recognition and QR/barcode reading in Hardiye.