Extract Data from Tables in PDFs and Screenshots (AI Guide 2026)
Tables are high-signal. But once they’re locked inside a PDF or image, they become “dead data.”
This guide (Eyesme example) shows how to extract tables into usable formats and avoid common errors.
Table extraction vs OCR
- OCR → plain text
- table extraction → rows/columns (CSV/JSON)
If your goal is sorting, summing, filtering, or comparing, you want table extraction.
6-step workflow
Step 1: Capture tightly
Crop to the table only. Avoid UI noise.
Step 2: Specify output format
- “Extract this table as CSV with headers.”
- “Extract as JSON (one object per row).”
Step 3: Validate quickly
Check:
- row/column count
- totals vs sum
- decimals/commas
- units/currency
Step 4: Fix common issues
- multi-row headers → ask for normalized headers
- merged cells → ask for key-value rows
- missing units → include the unit region or specify it
Step 5: Analyze the extracted data
- “Sum column X, top 5 items, flag outliers.”
Step 6: Save for reuse
Import CSV into Sheets/Excel/Notion.
High-frequency use cases
- bills/invoices: AI Bill & Invoice Analysis (2026)
- financial reports: AI Financial Report Analysis (2026 Guide)
- research results: How to Read Research Papers Faster with AI
Bottom line
Don’t leave data trapped in images. Extract it, validate it, and use it.