Should I use OCR or just try to parse PDFs with code?

Usually: try to parse the PDF first, use OCR only when needed.

Rule of thumb

• Text-based PDFs (generated from Word, LaTeX, web, export):
  Use PDF parsing / text extraction. It’s faster, cheaper, and more accurate.
• Scanned/image PDFs (pages are just images):
  Use OCR.
• Mixed PDFs (some real text, some scanned pages):
  Use both, page by page if possible.

Why not OCR everything?

OCR is:

• slower
• more expensive
• more error-prone than native text extraction
• worse for tables, symbols, and layout unless heavily tuned

Why not just parse everything?

PDF parsing works well only if the PDF actually contains selectable text. It can fail on:

• scanned documents
• image-only PDFs
• weird encodings
• complex layouts
• some tables/forms

Best practical approach

1. Detect whether the PDF has extractable text
   • Try extracting text from a page.
   • If you get little or nothing, suspect scanned content.
2. If text exists, parse it
   • Use a PDF text extraction library.
3. If text is missing or garbage, OCR it
   • Convert pages to images, then run OCR.

Common tools

• Parsing: pdfplumber, PyMuPDF (fitz), pdfminer.six
• OCR: Tesseract, AWS Textract, Google Document AI, Azure Form Recognizer

A good hybrid strategy

For most production systems:

• Extract embedded text when available
• OCR only pages that fail extraction
• Preserve layout info if you need tables/forms

If you want, I can help you with a simple decision tree or sample Python code to auto-detect whether a PDF should be parsed or OCR’d.