Unlock the Power of PDFs: Introducing olmOCR for AI-Powered Document Processing
Tired of wrestling with inaccessible PDF documents? olmOCR, a cutting-edge toolkit from AllenAI, is here to revolutionize how you interact with PDFs, leveraging the power of language models. This open-source solution provides state-of-the-art natural text parsing and unlocks hidden insights within your documents.
What is olmOCR and Why Should You Use It?
olmOCR is a comprehensive toolkit designed for training language models to extract and process text from PDF documents, even those with complex layouts or poor image quality. This allows for a far greater text extraction from documents that previously may have been ignored.
Here's what olmOCR brings to the table:
- ChatGPT 4o Integration: Cutting-edge prompting strategy for exceptionally accurate natural text parsing.
- Side-by-Side Evaluation: Easily compare different pipeline versions to optimize your processing workflow.
- Advanced Filtering: Cleans your data by removing SEO spam and non-English documents.
- Scalable Processing: Convert millions of PDFs using Sglang with efficient GPU utilization.
Key Features and Benefits of Using olmOCR
- Improved OCR Accuracy: Leverage the power of Qwen2-VL and Molmo-O fine-tuning for unparalleled accuracy.
- Efficient Data Extraction: Extract text and data from PDFs at scale, saving time and resources.
- Seamless Integration: Easily incorporate olmOCR into your existing data pipelines.
- Open-Source and Customizable: Adapt the toolkit to your specific needs and contribute to the community.
Getting Started with olmOCR: Installation and Local Usage
Ready to unlock the potential of your PDFs? Here's how to get started with olmOCR:
1. Prerequisites:
- Recent NVIDIA GPU (at least 20 GB of GPU RAM) ideal for PDF processing.
- 30 GB of free disk space.
- Poppler-utils and additional fonts for rendering PDF images.
2. Installation (Ubuntu/Debian):
3. Conda Environment Setup:
4. (Optional) Sglang Installation for GPU Inference:
5. Local Usage Example:
You can also convert multiple PDFs by using the following command:
Scaling Up: Multi-Node Cluster Usage with olmOCR
For large-scale PDF processing, olmOCR supports multi-node clusters using AWS S3 for storage and coordination. This makes it ideal for organizations dealing with massive document archives.
Example Command (First Worker Node):
Example Command (Subsequent Worker Nodes):
Diving Deeper: Exploring the olmOCR Pipeline
The olmocr.pipeline module provides extensive options for customizing your PDF processing workflow.
Key Parameters:
--pdfs: Specifies the path to the PDF files to process. Can be a glob pattern or a file containing a list of paths.--workspace: The location where work will be stored. Can be a local folder or an S3 path.--apply_filter: Apply filtering for English PDFs to reduce noise and SEO spam.--model: Specifies model paths to use for processing.--workers: Sets the number of worker threads to use.
For complete documentation, run:
License and Citation
olmOCR is licensed under Apache 2.0, promoting open collaboration and innovation.
Citation:
@misc { olmocr,
title = { {olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
author = { Jake Poznanski and Jon Borchardt and Jason Dunkelberger and Regan Huff and Daniel Lin and Aman Rangapur and Christopher Wilhelm and Kyle Lo and Luca Soldaini},
year = { 2025},
eprint = { 2502.18443},
archivePrefix = { arXiv},
primaryClass = { cs.CL},
url = { https://arxiv.org/abs/2502.18443},
}
Unlock the insights hidden within your PDFs today with olmOCR!