Skip to main content

​
Why PDF Processing Matters

80% of enterprise data lives in unstructured formats — PDFs, scans, reports. Before any of it can be retrieved by your RAG system, it needs to be extracted into clean text. The challenge: PDFs come in two fundamentally different forms (digital and scanned), and tables are notoriously hard to parse in both. This page covers how to extract text from PDFs reliably. For image processing and long document strategies, see Images & Long Documents.

​
PDF Processing: Digital vs. Scanned

Not all PDFs are created equal.

​
Digital PDFs (Text-Based)

​
Scanned PDFs (Image-Based)

​
Combined PDF Processing Pipeline

​
Table Extraction: The Hard Problem

Tables are notoriously difficult for OCR and even digital PDFs. Why tables are hard:
  • OCR sees tables as disconnected text blocks
  • Column alignment information is lost
  • Multi-line cells get fragmented
  • Headers vs. data distinction unclear
Solutions:

​
1. Specialized Table Extractors

​
2. Vision-Language Models for Tables

Modern approach: Use multimodal models to “see” the table. Production recommendation:
  1. Try specialized extractor (camelot) first - fast and accurate for clean tables
  2. Fall back to vision model for complex/messy tables - slower but more robust
  3. Store both raw table and structured version in metadata