Convert dense PDFs into LLM-ready text and page-aligned markdown with olmOCR
Use olmOCR when an agent needs to turn scanned or layout-heavy documents into clean markdown or text before chunking, search, extraction, or citation workflows.
What it does
Convert dense PDFs into LLM-ready text and page-aligned markdown with olmOCR
Use olmOCR when an agent needs to turn scanned or layout-heavy documents into clean markdown or text before chunking, search, extraction, or citation workflows.
Prerequisites
Python 3.11, pip or conda, poppler-utils, optional NVIDIA GPU for local inference
Installation
Use the upstream install or setup path that matches your environment:
- conda create -n olmocr python=3.11
- conda activate olmocr
- pip install olmocr
- pip install olmocr[gpu] --extra-index-url https://download.pytorch.org/whl/cu128
Requirements and caveats from upstream:
- (Based on a 7B parameter VLM, so it requires a GPU)
- June 17, 2025 - v0.1.75 - Switch from sglang to vllm based inference pipeline, updated docker image to CUDA 12.8.
- May 23, 2025 - v0.1.70 - Official docker support and images are now available! See Docker usage
Basic usage or getting-started notes:
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System Dependencies
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You will need to install poppler-utils and additional fonts for rendering PDF images.
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bash
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Extracted from upstream docs: https://raw.githubusercontent.com/allenai/olmocr/HEAD/README.md
Documentation
Source
Capabilities
Install
Quality
deterministic score 0.45 from registry signals: · indexed on github topic:agent-skills · 8 github stars · SKILL.md body (1,438 chars)