DocVerify — Photo ID Compliance

ID documents vary widely in layout, print quality, and scan condition, so OCR extraction needed confidence scoring: low-quality or ambiguous extractions get flagged for human review instead of silently passing or failing compliance checks. DocVerify validates government-issued photo ID documents, extracting fields via OCR and checking them against formatting and compliance rules — automating a process that was previously manual document review.

What I Built

• OCR extraction pulling name, ID number, dates, and photo region from scanned documents
• Compliance validation checking format, expiry, and field presence against defined rules
• Confidence scoring flagging low-confidence extractions for manual review rather than auto-approving them
• Audit trail logging every validation decision for accountability

Tech Stack

• Language: Python
• OCR: Tesseract / cloud OCR API
• Computer Vision: OpenCV
• Backend: Flask REST API

Key Decisions

• Added confidence scoring to OCR extraction rather than treating every extraction as pass/fail, so low-quality or ambiguous scans got routed to human review instead of silently passing or failing compliance
• Logged every validation decision to an audit trail, since compliance tooling needs accountability for how each document was assessed, not just a final yes/no output

Outcome / Impact

Automated a manual document-review process, cutting per-document review time significantly while keeping a human-in-the-loop safety net for uncertain cases.