PDF to Markdown Converter

Extract entire PDF content as clean, structured Markdown optimized for LLM context windows.

Quick Start

# Install dependencies (fast mode)
uv pip install pymupdf pymupdf4llm

# Convert a PDF
python pdf_to_md.py document.pdf

# Accurate tables (IBM Docling AI)
uv pip install docling docling-core
python pdf_to_md.py document.pdf --docling

Two Extraction Modes

Fast Mode (default)

Uses PyMuPDF with text-based table detection. Good for most documents.

python pdf_to_md.py report.pdf
# Output: report.md (with images/ directory if PDF has images)

• Speed: ~0.05 sec/page
• Tables: text-strategy detection (handles borderless tables)
• Images: extracted at source resolution
• Best for: simple layouts, text-heavy docs, quick processing

Accurate Mode (Docling)

Uses IBM's TableFormer AI model for ~93.6% table extraction accuracy.

python pdf_to_md.py report.pdf --docling
# First run downloads AI models (~2-3 min one-time setup)

• Speed: ~1 sec/page
• Tables: AI-powered structure detection (complex/borderless)
• Images: configurable resolution (default 4x)
• Best for: financial reports, scientific papers, complex layouts

Persistent Caching

Extractions are cached in ~/.cache/pdf-to-markdown/. Cache is keyed by file content hash + extraction mode — the same PDF extracts once and reuses forever.

# View cache stats
python pdf_to_md.py --cache-stats

# Clear cache for one PDF
python pdf_to_md.py document.pdf --clear-cache

# Clear entire cache
python pdf_to_md.py --clear-all-cache

Cache invalidates automatically when:

• Source PDF content changes (hash-based detection)
• Extractor version bumps (logic improvements)
• You explicitly clear it

Output Format

---
source: document.pdf
total_pages: 42
extracted_at: 2026-03-23T22:00:00
images_dir: images
---

# Document Title

Content here...

| Column A | Column B |
|----------|----------|
| data     | data     |

![Figure](images/figure_0001.png)

**[Image: figure_0001.png (1920x1080, 245.3KB)]**

---

## Extracted Images

| # | File | Dimensions | Size |
|---|------|------------|------|
| 1 | figure_0001.png | 1920x1080 | 245.3KB |

Usage Patterns

Basic Conversion

python pdf_to_md.py input.pdf                    # → input.md
python pdf_to_md.py input.pdf output.md          # custom output path
python pdf_to_md.py input.pdf --docling           # accurate tables
python pdf_to_md.py input.pdf --no-progress       # suppress progress

Programmatic Usage

from extractor import extract_pdf_fast, extract_pdf_docling, get_page_count

# Fast extraction
pages = get_page_count("report.pdf")
markdown = extract_pdf_fast("report.pdf", image_dir="./images")

# Accurate extraction with images
markdown, image_paths = extract_pdf_docling(
    "report.pdf",
    output_dir="./images",
    images_scale=4.0,
    show_progress=True,
)

With Caching (recommended)

from pdf_to_md import CacheManager, ExtractionConfig, ImageManager

cache = CacheManager()
config = ExtractionConfig(pdf_path="report.pdf", docling=False)

valid, cache_key = cache.is_valid(config)
if valid:
    result = cache.load(cache_key)
    markdown = result.markdown
else:
    # Extract and cache
    markdown = extract_pdf_fast("report.pdf")
    cache.save(cache_key, ExtractionResult(
        markdown=markdown, image_dir=None, total_pages=pages
    ), config)

Batch Processing

# Convert all PDFs in a directory
for f in *.pdf; do python pdf_to_md.py "$f"; done

# Parallel with xargs
ls *.pdf | xargs -P4 -I{} python pdf_to_md.py {}

Architecture

pdf_to_md.py          — CLI entry point, cache orchestration, output formatting
extractor.py          — Extraction backends (fast + docling)
~/.cache/pdf-to-markdown/
  └── {hash16}/
      ├── metadata.json    — source info, mtime, version
      ├── full_output.md   — cached markdown
      └── images/          — cached extracted images

Key Classes

Class	Purpose
ExtractionConfig	PDF path, mode (fast/docling), image scale
ExtractionResult	Markdown output, image dir, page count, cache flag
CacheManager	Content-hash keying, atomic writes, validation
ImageManager	Temp dir tracking, image metadata, path rewriting

Cache Key Generation

SHA256(file_size | SHA256(first_64KB + last_64KB) | mode)[:16]

This means:

• Same PDF content → same cache key (regardless of filename)
• Different extraction modes → different cache entries
• File renames don't invalidate cache
• Content changes always invalidate

Dependencies

Package	Mode	Purpose
pymupdf	Both	PDF parsing, image extraction
pymupdf4llm	Fast	Markdown conversion with table detection
docling	Accurate	IBM document AI pipeline
docling-core	Accurate	Document model and export

Install

# Fast mode only
uv pip install pymupdf pymupdf4llm

# Both modes
uv pip install pymupdf pymupdf4llm docling docling-core

Image Handling

Images are extracted to an images/ directory next to the output markdown file. References use relative paths for portability.

• Fast mode: extracts all embedded images via xref deduplication
• Docling mode: extracts figures at configurable resolution (default 4x)
• Cache preserves images and copies them to output location on cache hit
• CMYK images auto-convert to RGB

Tips

• Use --docling for financial reports, academic papers, or any document with complex table layouts
• Cache stats help monitor disk usage: --cache-stats
• For LLM context windows, the metadata header provides page count and source info
• Page breaks use <!-- PAGE_BREAK --> sentinels (not -----) to avoid false splits
• Extractor version (3.3.0) is tracked in cache to auto-invalidate on upgrades