PDF Reader

Extract content from PDF files using pdftotext (Poppler) for text and pdfplumber (Python) for tables and structured extraction.

Quick Reference

Task	Tool	Command
Full text	pdftotext	pdftotext file.pdf -
Text with layout	pdftotext	pdftotext -layout file.pdf -
Specific pages	pdftotext	pdftotext -f 3 -l 5 file.pdf -
Tables	pdfplumber	python3 scripts/extract.py tables file.pdf
Metadata	pdfinfo	pdfinfo file.pdf
Page count	pdfinfo	pdfinfo file.pdf | grep Pages
Images	pdfimages	pdfimages -list file.pdf
Fonts	pdffonts	pdffonts file.pdf
OCR (scanned PDF)	tesseract	python3 scripts/extract.py ocr file.pdf
Smart text + OCR	extract.py	python3 scripts/extract.py text file.pdf
Quick survey	extract.py	python3 scripts/extract.py scan file.pdf

Workflow

Step 1: Get the PDF

If the user provides a URL, download it first:

curl -sL "URL" -o /tmp/document.pdf

Verify it's a valid PDF:

file /tmp/document.pdf  # should say "PDF document"
pdfinfo /tmp/document.pdf  # metadata + page count

Step 2: Choose Extraction Method

Plain text (most cases):

pdftotext file.pdf -

This pipes output to stdout. For large PDFs, use page ranges:

pdftotext -f 1 -l 10 file.pdf -    # pages 1-10

Layout-preserving text (columns, formatted docs):

pdftotext -layout file.pdf -

Use -layout when the PDF has multi-column layouts, tables rendered as text, or precise spacing that matters.

Tables (structured data):

python3 scripts/extract.py tables file.pdf

Or inline with pdfplumber:

import pdfplumber

pdf = pdfplumber.open("file.pdf")
for i, page in enumerate(pdf.pages):
    tables = page.extract_tables()
    for table in tables:
        print(f"\n--- Table on page {i+1} ---")
        for row in table:
            print(" | ".join(str(cell or "") for cell in row))
pdf.close()

Metadata only:

pdfinfo file.pdf

Returns: title, author, creator, producer, page count, page size, dates.

Step 3: Handle Large PDFs

For PDFs over ~50 pages, don't dump everything at once:

1. Get page count: pdfinfo file.pdf | grep Pages
2. Extract in chunks: pdftotext -f 1 -l 20 file.pdf -
3. Process chunk, then continue: pdftotext -f 21 -l 40 file.pdf -

For targeted extraction (searching for specific content):

# Extract all text, grep for relevant sections
pdftotext file.pdf - | grep -n -i "keyword"

# Then extract the specific page range
pdftotext -f PAGE -l PAGE file.pdf -

Step 4: Handle Scanned PDFs (OCR)

If pdftotext returns empty or garbled output, the PDF is likely scanned.

Detection:

python3 scripts/extract.py scan file.pdf   # reports scanned pages
pdffonts file.pdf                           # empty = image-based

Smart extraction (auto-fallback):

text mode automatically detects scanned pages and OCRs them:

python3 scripts/extract.py text file.pdf

Pages with selectable text extract normally. Pages without selectable text fall back to OCR via Tesseract. No manual detection needed.

Force OCR on all pages:

python3 scripts/extract.py ocr file.pdf
python3 scripts/extract.py ocr file.pdf --pages 1-5
python3 scripts/extract.py ocr file.pdf --dpi 400        # higher quality
python3 scripts/extract.py ocr file.pdf --lang eng+nor   # multi-language

OCR options:

• --dpi 300 — resolution for page-to-image conversion (default: 300, higher = slower but better)
• --lang eng — Tesseract language pack (default: eng). Use + for multiple: eng+nor+deu
• --pages 1-5 — limit to specific pages (recommended for large PDFs)

Available language packs:

tesseract --list-langs

Install additional languages via Homebrew:

brew install tesseract-lang    # all languages

Step 5: Handle Other Edge Cases

Mixed PDFs (some pages scanned, some not):

Just use text mode — it handles mixed PDFs automatically:

python3 scripts/extract.py text file.pdf

Selectable pages extract instantly, scanned pages get OCR'd. The output is tagged so you know which pages used OCR.

Password-protected PDFs:

pdftotext -upw "password" file.pdf -   # user password
pdftotext -opw "password" file.pdf -   # owner password

Encoding issues (garbled output):

pdftotext -enc UTF-8 file.pdf -

Extract images:

pdfimages -png file.pdf /tmp/images/img   # extracts as PNG
pdfimages -list file.pdf                  # list images without extracting

Decision Tree

Is it a URL? → curl -sL "URL" -o /tmp/doc.pdf
            ↓
Run: python3 scripts/extract.py scan file.pdf
            ↓
All pages have selectable text?
  YES → pdftotext file.pdf -           (fast, simple)
  NO  → python3 scripts/extract.py text file.pdf  (auto OCR fallback)
            ↓
Need tables?
  YES → python3 scripts/extract.py tables file.pdf

Tips

• Start with scan on unknown PDFs — it reports pages, tables, scanned detection, and a preview
• pdftotext is fastest for normal PDFs — try it first
• Use -layout for multi-column documents (academic papers, reports)
• pdfplumber is better for tables — it understands cell boundaries
• text mode auto-detects scanned pages and OCRs only those — preferred over raw pdftotext for unknown PDFs
• ocr mode is for forcing OCR on everything (useful when text extraction gives garbled output despite appearing selectable)
• Higher --dpi gives better OCR accuracy but is slower (300 is a good default, 400+ for small text)
• For PDFs from URLs, always download to /tmp/ first — don't pipe curl to tools
• Large PDF text output may exceed context limits — use --pages to extract in ranges