Problem or Motivation
Manual invoice handling was slow and error-prone. The system needed to process diverse layouts, preserve auditability, and integrate into secure enterprise workflows.
What I Built
- Multi-stage OCR pipeline with document type detection and layout-aware parsing.
- Validation layer for totals, tax fields, and vendor details before approval routing.
- Export layer that pushed normalized outputs to finance APIs and analytics dashboards.
Tools and Technologies Used
- Python, PyTorch, PaddleOCR, OpenCV
- Docker, CI workflows, secure deployment practices
- REST APIs, structured data pipelines, monitoring hooks
My Role and Contributions
- Owned pipeline design from ingestion to model output validation.
- Implemented fallback logic for low-confidence documents.
- Collaborated with product and operations teams to reduce manual review load.
Challenges and Lessons Learned
- OCR quality varied heavily by template and scan quality.
- Confidence thresholds needed calibration to balance precision and throughput.
- Operational logging is critical for trust and debugging in finance workflows.
Gallery or Screenshots
