PDF Processing

Process PDF files using command-line tools and Python libraries. For scanned PDFs, convert pages to images and have the LLM read them using its built-in image understanding.

Prerequisites

Before running any Python scripts, activate the opencode virtual environment:

source ~/.local/opencode-venv/bin/activate

Then use python3 scripts/... normally.

Available Tools

Decision Guide

Need to process a PDF?
│
├─ Extract everything from a PDF?
│  └─ Dispatch the `pdf-extractor` agent.
│     It converts all pages to images, reads each one, and stores
│     per-page results in extracted_data/<pdf_name>/page_NNN/content.md
│     along with images and a summary.
│
├─ Quick text from a normal PDF?
│  ├─ Simple: pdftotext input.pdf output.txt
│  └─ With page numbers: python3 scripts/extract_text.py input.pdf
│
├─ Extract tables?
│  └─ python3 scripts/extract_tables.py input.pdf -o ./tables/
│
├─ Fill a form?
│  ├─ Check fields: python3 scripts/check_forms.py input.pdf
│  └─ Fill: python3 scripts/fill_form.py input.pdf fields.json output.pdf
│
├─ Read a scanned PDF (quick)?
│  ├─ Convert: python3 scripts/to_images.py scanned.pdf -o ./pages/ --dpi 200
│  └─ Read the images and extract what you need
│
├─ Merge/Split/Rotate?
│  └─ See pypdf examples below
│
└─ Large file (>50MB)?
   ├─ Quick text: pdftotext (fastest)
   └─ Page ranges: pdftotext -f N -l M input.pdf

Full PDF Extraction

For complete extraction of all data from a PDF (text, tables, forms, structure), dispatch the pdf-extractor agent. It:

1. Converts all pages to PNG images
2. Reads each image using LLM vision
3. Extracts text, tables, key facts, form fields, and notes
4. Per-page output in extracteddata/<pdfname>/page_NNN/:

- page_NNN.png — the page image - content.md — all text and extracted facts as markdown - Other LLM-legible files as needed

1. Creates summary.md with document-level overview

When to use the extractor agent

• Multi-page documents where you need all the data
• Scanned PDFs or mixed scanned/digital PDFs
• Documents with complex layouts (tables, columns, forms)
• When you need a structured, browsable folder tree of results

Quick extraction (no agent)

For a single page or a quick glance, use the manual steps below.

Reading Scanned PDFs (Quick, No Agent)

When a PDF is scanned (no selectable text), convert its pages to images and let the LLM read them.

Step 1: Detect if PDF is scanned

pdftotext document.pdf - | head -20

If output is empty or whitespace, the PDF is scanned.

Step 2: Convert pages to images

# All pages at 200 DPI (sufficient for most documents)
python3 scripts/to_images.py document.pdf -o ./pdf-pages/ --dpi 200

# Higher quality for handwritten or low-quality scans
python3 scripts/to_images.py document.pdf -o ./pdf-pages/ --dpi 300

Step 3: Read the images

Reference the generated PNG files directly — the LLM can read them natively. Ask it to extract text, tables, fields, or answer questions about the document content.

Step 4: Clean up

rm -rf ./pdf-pages/

Tips for scanned PDFs

• 200 DPI works for most documents
• 300 DPI for handwritten content or blurry scans
• For large PDFs, convert pages in batches (use pypdf to split first, then convert)
• Always clean up the generated image directory after reading

Text Extraction

Quick extraction (pdftotext)

# Simple extraction
pdftotext input.pdf output.txt

# Single page
pdftotext -f 3 -l 3 input.pdf page3.txt

# With layout preservation
pdftotext -layout input.pdf output.txt

# Page range
pdftotext -f 5 -l 10 input.pdf pages_5_10.txt

With page numbers (script)

python3 scripts/extract_text.py input.pdf

Get PDF info

pdfinfo input.pdf

Table Extraction

Script (recommended)

python3 scripts/extract_tables.py input.pdf -o ./tables/

Python (inline)

import pdfplumber
import csv

with pdfplumber.open("input.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            with open(f"page{i+1}_table{j+1}.csv", "w", newline="") as f:
                writer = csv.writer(f)
                for row in table:
                    writer.writerow([cell or "" for cell in row])

Form Handling

Check for fillable fields

python3 scripts/check_forms.py input.pdf

Fill a form

python3 scripts/fill_form.py input.pdf fields.json output.pdf

Where fields.json contains:

{
  "field_name": "John Smith",
  "email": "[email protected]",
  "date": "2026-05-04"
}

PDF Manipulation (pypdf)

Merge PDFs

from pypdf import PdfReader, PdfWriter

writer = PdfWriter()
for pdf_file in ["file1.pdf", "file2.pdf", "file3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as f:
    writer.write(f)

Split into individual pages

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1:03d}.pdf", "wb") as f:
        writer.write(f)

Rotate pages

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()
for page in reader.pages:
    page.rotate(90)  # 90, 180, or 270 degrees
    writer.add_page(page)

with open("rotated.pdf", "wb") as f:
    writer.write(f)

Remove pages

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()
pages_to_keep = {0, 1, 4, 5}  # 0-indexed
for i, page in enumerate(reader.pages):
    if i in pages_to_keep:
        writer.add_page(page)

with open("trimmed.pdf", "wb") as f:
    writer.write(f)

Metadata

python3 scripts/metadata.py input.pdf

Convert to Images

python3 scripts/to_images.py input.pdf --dpi 200

Large Files

For large PDFs (>50MB):

• Use pdftotext for quick extraction (faster than Python)
• Process pages in batches with pdfplumber
• Use -f and -l flags with pdftotext for specific page ranges

Writing Large Output Files

If extracting text from a very large PDF, write the output incrementally (~1000 tokens per edit) rather than in a single pass.

Common Patterns

Search for text in a PDF

pdftotext input.pdf - | grep -n "search term"

Compare two PDFs (text diff)

pdftotext doc1.pdf - > /tmp/doc1.txt
pdftotext doc2.pdf - > /tmp/doc2.txt
diff /tmp/doc1.txt /tmp/doc2.txt

Edge Cases

Encrypted PDFs

from pypdf import PdfReader

reader = PdfReader("encrypted.pdf")
if reader.is_encrypted:
    reader.decrypt("")  # Try empty password first

Corrupted or Invalid PDFs

try:
    with pdfplumber.open("file.pdf") as pdf:
        # process...
except Exception as e:
    print(f"Error processing PDF: {e}")

Very Large PDFs (>100MB)

• Use pdftotext for speed
• Process in page batches with pdfplumber
• Avoid loading entire file into memory

Mixed Content (Some pages scanned, some not)

1. First try standard extraction (pdftotext)
2. Check which pages returned empty text
3. Convert only those pages to images and have the LLM read them