deepseek-ocr-cli

DeepSeek OCR CLI

Command-line tool for OCR using DeepSeek vision models. Supports Ollama (local) and vLLM (GPU server) backends.

Features

• Multi-backend: Ollama (local, free) and vLLM (OpenAI-compatible API)
• Supports PDFs and images (JPG, PNG, WEBP, GIF, BMP, TIFF)
• Per-document output folders with figures
• Batch processing with incremental resume (skips already-processed files)
• Retry with exponential backoff for transient failures
• Parallel page processing for faster PDF OCR
• --dry-run to preview files before processing
• Clean markdown output with HTML tables converted to markdown

Choosing an OCR tool

This is one of five OCR CLI tools with a shared design: clean Markdown output, batch processing, and figure extraction. Pick based on your constraints:

Tool	Engine	Runs	Cost	Best for
deepseek-ocr-cli (this repo)	DeepSeek vision	Local (Ollama / vLLM)	Free	General-purpose local OCR with multi-backend flexibility
gemini-ocr-cli	Google Gemini	Cloud API	Free tier / Pay-per-use	Fast cloud OCR with concurrent processing
marker-ocr-cli	Marker (Surya + Texify)	Local	Free	Academic papers with equations, tables, complex layouts
mistral-ocr-cli	Mistral OCR API	Cloud API	~$1/1k pages	Structured extraction (tables, headers, footers)
nougat-ocr-cli	Meta Nougat	Local (GPU)	Free	Academic papers, GPU-accelerated batch processing

Requirements

• Python 3.10+
• Ollama installed and running (for Ollama backend)
• deepseek-ocr model pulled in Ollama

Installation

1. Install Ollama

# macOS/Linux
brew install ollama

# Or download from https://ollama.ai

2. Pull the DeepSeek-OCR model

ollama pull deepseek-ocr

3. Install the CLI

pip install deepseek-ocr-cli

Quick Start

# Process a single image
deepseek-ocr document.jpg

# Process a PDF
deepseek-ocr paper.pdf

# Process all files in a directory
deepseek-ocr ./documents/ --recursive

# Preview files without processing
deepseek-ocr ./documents/ --dry-run

# Custom output directory
deepseek-ocr doc.pdf -o ./results/

# Use vLLM backend
deepseek-ocr paper.pdf --backend vllm --vllm-url http://gpu-server:8000/v1

# Parallel processing for faster PDF OCR
deepseek-ocr large-document.pdf -w 2

# Extract and analyze embedded figures
deepseek-ocr paper.pdf --analyze-figures

# Quiet mode (paths only, for scripting)
deepseek-ocr paper.pdf -q

CLI Options

deepseek-ocr [OPTIONS] INPUT_PATH

Options:
  -o, --output-dir PATH           Output directory for results
  -r, --recursive                 Recursively process directories
  --model TEXT                    Model name (default: deepseek-ocr)
  --prompt TEXT                   Custom prompt for OCR
  --task [convert|ocr|layout|extract|parse]
                                  OCR task type
  --extract-images                Extract and save page images from PDFs
  --no-metadata                   Exclude metadata from output
  --dpi INTEGER                   PDF rendering DPI (default: 200)
  -w, --workers INTEGER           Parallel workers for PDF pages (default: 1)
  --analyze-figures               Extract and analyze embedded figures with AI
  --max-dim INTEGER               Max image dimension (default: 1920, 0 to disable)
  --backend [ollama|vllm]         Backend to use (default: ollama)
  --vllm-url TEXT                 vLLM API URL (default: http://localhost:8000/v1)
  --reprocess                     Force reprocessing of already-done files
  --dry-run                       Preview files without processing
  -q, --quiet                     Suppress output, print paths only
  --verbose                       Enable verbose output
  --help                          Show this message and exit.

Commands

process (default)

Process documents and images with OCR. The process subcommand is optional:

deepseek-ocr document.pdf
# equivalent to
deepseek-ocr process document.pdf

info

Show system and configuration information.

deepseek-ocr info

Output Format

Each document gets its own folder:

output/
└── document/
    ├── document.md          # OCR markdown
    └── figures/             # Extracted figures (if --analyze-figures)
        └── page1_fig1.png

The markdown includes metadata:

---
source: /path/to/document.pdf
processed: 2025-12-01T15:30:00
pages: 3
processing_time: 18.45s
model: deepseek-ocr
backend: ollama
---

## Page 1

[Extracted content...]

Batch Resume

Batch processing saves metadata.json in the output directory. On re-run, already-processed files are skipped automatically. Use --reprocess to force reprocessing.

Configuration

Create a .env file or set environment variables with DEEPSEEK_OCR_ prefix:

DEEPSEEK_OCR_BACKEND=ollama
DEEPSEEK_OCR_MODEL_NAME=deepseek-ocr
DEEPSEEK_OCR_OUTPUT_DIR=output
DEEPSEEK_OCR_OLLAMA_URL=http://localhost:11434
DEEPSEEK_OCR_VLLM_BASE_URL=http://localhost:8000/v1
DEEPSEEK_OCR_MAX_DIMENSION=1920
DEEPSEEK_OCR_MAX_RETRIES=3
DEEPSEEK_OCR_RETRY_DELAY=1.0

Programmatic Usage

from pathlib import Path
from deepseek_ocr import create_backend, OCRProcessor

backend = create_backend(backend_type="ollama", model_name="deepseek-ocr")
backend.load_model()

processor = OCRProcessor(
    backend=backend,
    output_dir=Path("./results"),
    workers=2,
)

result = processor.process_file(Path("document.pdf"))
print(result.output_text)

processor.save_result(result)
backend.unload_model()

Troubleshooting

Ollama not running

ollama serve

Model not found

ollama pull deepseek-ocr

Check status

deepseek-ocr info

License

MIT License - see LICENSE for details.