Document Automation Engineer: AI OCR & Regex Data Extraction.

The primary objective of this project was to modernize data operations for a humanitarian NGO struggling with high volumes of physical forms, paper beneficiary logs, and handwritten field reports. The organization faced severe bottlenecks due to slow, manual data entry, which often introduced transcription errors and created a high risk of duplicate or corrupted beneficiary data during spreadsheet imports. To solve this, I engineered an automated document processing pipeline capable of ingesting these unstructured paper records and converting them into structured, queryable database formats.

To tackle the varying scan qualities and inconsistent field layouts, I built a robust extraction layer utilizing Python and Tesseract OCR. By combining these tools with pdfplumber and custom rule-based extraction scripts, the system intelligently navigated chaotic document structures. It accurately parsed targeted data points such as IDs, dates, and financial figures directly from the scanned files, effectively converting static images into machine-readable text components.

Once the raw text was extracted, it passed through a sophisticated transformation layer powered by regex-driven validation scripts. This logic automatically cleaned, normalized, and verified the parsed entries against strict business rules, performing automated cross-checks such as confirming that extracted line items mathematically matched the stated subtotals. The pipeline also featured dynamic confidence scoring and error-flagging logic. Highly legible fields would clear with 93% to 99% confidence, while more complex or degraded sections with lower scores (e.g., 87%) were automatically flagged and isolated for human review, ensuring no bad data slipped through.

The final pipeline culminated in an automated end-to-end export to structured CSV and database storage. By replacing the manual transcription bottleneck with this AI-driven OCR architecture, the solution cut data entry time by over 80%. Ultimately, this streamlined the NGO's reporting capabilities, eliminated the risk of database corruption, and ensured that all beneficiary data remained highly reliable and fully audit-ready for their donors.