marker-ocr-cli 0.1.0

CLI for PDF text extraction using Marker's layout-aware pipeline

Marker OCR CLI

A command-line tool for OCR processing using Marker's layout-aware pipeline. Extract text, equations, tables, and figures from PDFs with high accuracy.

Installation

Requires Python 3.11+ and a GPU (recommended).

pip install marker-ocr-cli

Or from source:

git clone https://github.com/r-uben/marker-ocr-cli.git
cd marker-ocr-cli
uv sync

Quick start

# Process a single file
marker-ocr paper.pdf

# Process a directory
marker-ocr ./papers/ -o ./results/

# Preview what would be processed (no model loading)
marker-ocr ./papers/ --dry-run

# Process specific pages
marker-ocr paper.pdf --pages 0-5

# Force OCR on all pages
marker-ocr paper.pdf --force-ocr

Options

Usage: marker-ocr [OPTIONS] INPUT_PATH

Options:
  -o, --output-dir PATH           Output directory (default: <input_dir>/marker_ocr_output/)
  --pages TEXT                    Page range (e.g., '0-5' or '1,3,5')
  --force-ocr                     Force OCR on all pages regardless of embedded text

  --reprocess                     Reprocess already-processed files
  --dry-run                       List files without loading models
  -q, --quiet                     Suppress all output except errors
  -v, --verbose                   Enable verbose/debug output
  --info                          Show system and device info
  --version                       Show version
  --help                          Show this message

Output structure

marker_ocr_output/
├── document_name/
│   ├── document_name.md        # OCR markdown (clean text only)
│   └── figures/                # extracted figures
│       ├── figure_1.png
│       └── figure_2.png
├── another_document/
│   └── ...
└── metadata.json               # processing stats, checksums, file list

How it works

Marker uses a pipeline of specialized models rather than a single end-to-end model:

• Surya -- layout detection and reading order
• Surya OCR -- text recognition
• Texify -- equation detection and LaTeX conversion

This approach is faster and more accurate than single-model solutions, especially for academic papers with complex layouts, equations, and tables.

Development

# Install dev dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Lint
uv run ruff check .

# Format
uv run ruff format .

# Type check
uv run mypy marker_ocr/ --ignore-missing-imports

Limitations

• Supported formats: PDF only (Marker processes PDFs natively)
• Models: ~4-5 GB VRAM (auto-downloads on first run)
• GPU recommended for reasonable speed (supports CUDA and MPS)

License

MIT License - see LICENSE for details.