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deepseek-ai/deepseek-ocr

★ 23,135PythonAudience · developerComplexity · 4/5MaintainedLicenseSetup · hard

TLDR

AI model that extracts text from images and documents using efficient vision processing, converting photos and PDFs into readable text or formatted Markdown.

Mindmap

mindmap
  root((repo))
    What it does
      Extract text from images
      Convert PDFs to Markdown
      Parse charts and figures
    How it works
      Efficient token compression
      Supports multiple resolutions
      GPU-accelerated processing
    Use cases
      Document digitization
      Receipt and form parsing
      Handwritten note extraction
    Tech stack
      Python
      CUDA
      Hugging Face
    Audience
      Researchers
      ML developers
      Document automation teams

Things people build with this

USE CASE 1

Extract text from scanned documents and PDFs to feed into AI systems or databases.

USE CASE 2

Digitize receipts, invoices, and forms by converting images to structured text.

USE CASE 3

Parse handwritten notes and convert them to searchable digital text.

USE CASE 4

Extract and recognize text from charts, diagrams, and figures in documents.

Tech stack

PythonCUDAPyTorchHugging Face

Getting it running

Difficulty · hard Time to first run · 1h+

CUDA/GPU setup and PyTorch compilation are the main bottlenecks; CPU-only fallback may be very slow.

Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

DeepSeek-OCR is an AI model from DeepSeek that reads text from images and documents. OCR stands for Optical Character Recognition, the ability to extract written text from a photo or scanned file. What makes this model different is its approach to handling document images: it compresses visual information very efficiently, using far fewer "vision tokens" (the units it processes) than typical models while still accurately reading text. The model can convert a document image to formatted Markdown text (preserving headings and structure), extract raw text from any image, parse figures and charts, and recognize text at various resolutions. It supports both small images (512x512 pixels) and large documents (up to 1280x1280), and can handle PDFs page by page. You would use this if you are building a pipeline to extract text from scanned documents, PDFs, photos of receipts, or handwritten notes, particularly for use cases like feeding documents into AI systems, databases, or search tools. It is designed for researchers and developers comfortable running AI models on GPU hardware. It requires Python and CUDA (NVIDIA GPU support) and can run at speeds around 2500 tokens per second on an A100 GPU. The model weights are available on Hugging Face.

Copy-paste prompts

Prompt 1
How do I set up DeepSeek-OCR on my GPU to start extracting text from PDF documents?
Prompt 2
Show me how to convert a batch of receipt images into structured text using DeepSeek-OCR.
Prompt 3
What's the best way to use DeepSeek-OCR to extract text from handwritten notes and preserve formatting?
Prompt 4
How can I integrate DeepSeek-OCR into a pipeline that feeds extracted document text into a vector database?
Prompt 5
What are the token efficiency gains of DeepSeek-OCR compared to other vision models for document processing?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.