OCR Document Automation
A validation-first OCR pipeline designed to produce structured outputs that are correct, explainable, and operationally safe. Details are intentionally public-safe.
100%field-mapping accuracy (defined set)
PythonOpenCVOCRValidationAuditability
Outcome
Reliable extraction with guardrails: correct mappings, explainable failures, and outputs suitable for downstream use and review.
Why it matters
OCR errors often look plausible. The system is built to catch silent failure modes early and to make issues easy to diagnose.
Approach (high level)
- Preprocessing: normalize scans for stability (public-safe summary).
- Extraction: OCR + mapping to a structured schema.
- Validation layers: format/geometry/constraints/cross-field rules before acceptance.
- Confidence & review: thresholds decide auto-accept vs human review.
- Auditability: traceable outputs and debuggable signals.
What Iād improve next
- Expand evaluation sets and add input-quality drift checks.
- Calibrate confidence thresholds to reduce review time without increasing risk.
- Standardize test fixtures and add regression coverage for known edge cases.
Note: project artifacts and client details may be restricted. I can discuss tradeoffs and engineering decisions at a high level.