deepseek-ai/DeepSeek-OCR

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23855 stars · 2203 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepSeek-OCR is an open vision-language model from DeepSeek AI that researches 'contexts optical compression' - encoding long text contexts as images through a vision encoder feeding an LLM decoder. The repository ships model weights, inference code for both Transformers and vLLM, and an accompanying arXiv paper.

Use cases

• extract text from scanned documents or images with an open-source OCR model
• compress long text contexts into images to extend LLM effective context
• run document understanding with a vision-language model
• serve an OCR model at scale with vLLM
• experiment with optical context compression research from the paper
• parse layouts, tables, and text from PDFs or screenshots

When to choose

• you need high-accuracy open-weights OCR or document parsing with GPU acceleration
• you want to reproduce or build on the contexts-optical-compression research
• you want a DeepSeek vision model with official upstream vLLM support

When to avoid

• you need a lightweight CPU-only OCR utility - this requires CUDA GPUs, flash-attention, and a heavy PyTorch/vLLM stack
• you want the newest iteration - DeepSeek-OCR2 was announced in January 2026 as the successor
• you need a turnkey OCR SaaS with preprocessing, batching UI, and export pipelines

Facets

library · maturity active

ocr machine-learning llm-inference image-processing computer-vision transformers artificial-intelligence machine-learning deep-learning large-language-models computer-vision image-processing python vision-language-model vision-encoder optical-context-compression context-compression model-weights inference-code vllm huggingface multimodal research-model deepseek mit-license natural-language-processing gpu linux

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markdown · JSON · MCP: product_card(name="deepseek-ai/DeepSeek-OCR")