How to Extract Tables from PDFs — A Complete Guide

Tables are the most information-dense part of most documents, and the hardest to extract accurately. Unlike running text, tables require understanding the spatial relationship between rows, columns, and cells.

Why Tables Are Hard

• No native structure: PDFs dont store table metadata — they only store rendering instructions
• Bordered vs borderless: Some tables have visible grid lines, others rely on whitespace and alignment
• Merged cells: Multi-row and multi-column headers break naive extraction approaches
• Scanned PDFs: Image-based PDFs require full OCR, not just text extraction

Option 1: ParseFlow (Recommended)

ParseFlow extracts tables automatically from any PDF or image. Tables are returned as arrays of row arrays — ready to import into spreadsheets or databases.

curl -X POST https://parseflow.cc/v1/document/analyze \
  -H "Authorization: Bearer ocrg_live_key" \
  -F "document=@report.pdf" \
  -F 'featureTypes=["TABLES"]'

Option 2: AWS Textract

Textract's AnalyzeDocument with tables enabled returns TABLE and CELL blocks with row/column indexes. However, it costs $15 per 1,000 pages just for table detection, and requires S3/Lambda infrastructure.

Option 3: Open Source (Camelot, Tabula)

Camelot and Tabula work well on digital PDFs with clear table borders. They are free but handle scanned PDFs poorly, require local infrastructure, and struggle with complex layouts.

Pricing Comparison for Table Extraction

ParseFlow: $5.00/1K pages — includes text + tables + forms together
Textract: $15.00/1K pages (tables only)
Document AI: $10.00/1K pages (layout parser)

Learn more about ParseFlow Table Extraction →