PDF Data Extraction & Structuring

I have a collection of printed documents that were previously scanned into PDF format. Now I need every data point inside those PDFs captured accurately and delivered in a fully searchable, analysis-ready file. The end goal is straightforward: transform static pages into clean, structured data so my team can run automated processing and analytics without manual re-keying.

Here is what I’m looking for:
• Apply reliable OCR and/​or manual key-in where OCR falls short to achieve near-perfect text accuracy.
• Preserve the original document order and clearly label each record so I can trace any value back to its source page.
• Output the final dataset in CSV or Excel (both if effortless for you), with logical column headers and consistent formatting.
• Include the corresponding machine-readable PDFs you generated, bookmarked for quick navigation.

Acceptance criteria
1. Random spot-checks across at least 10% of pages show 99% character accuracy.
2. No broken rows, merged columns, or missing fields in the exported sheet.
3. File naming follows: <DocID>_<PageRange>.<ext>.
4. Delivery is complete only when I can filter, sort, and run basic summaries on the file without errors.

You can lean on tools like Adobe Acrobat Pro, ABBYY FineReader, or any OCR pipeline you trust, as long as the results meet the accuracy target. If you already have scripts in Python (Tesseract, pdfplumber, Camelot, etc.) or other automation that speeds things up, all the better—just note what you used so the workflow is reproducible.

Turnaround is flexible for quality, but let me know your realistic timeline in your proposal. Looking forward to seeing how you’ll convert these PDFs into actionable data.