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A toolkit for converting PDFs and other image-based document formats into clean, readable, plain text format.
Try the online demo: https://olmocr.allenai.org/
Features:
olmOCR-Bench: We also ship a comprehensive benchmark suite covering over 7,000 test cases across 1,400 documents to help measure performance of OCR systems.
| Model | AR | OSM | TA | OS | HF | MC | LTT | Base | Overall Score |
|---|---|---|---|---|---|---|---|---|---|
| Marker v1.6.2 | 24.3 | 22.1 | 69.8 | 24.3 | 87.1 | 71.0 | 76.9 | 99.5 | 59.4 ± 1.1 |
| MinerU v1.3.10 | 75.4 | 47.4 | 60.9 | 17.3 | 96.6 | 59.0 | 39.1 | 96.6 | 61.5 ± 1.1 |
| Mistral OCR API | 77.2 | 67.5 | 60.6 | 29.3 | 93.6 | 71.3 | 77.1 | 99.4 | 72.0 ± 1.1 |
| olmOCR v0.1.68 (pipeline.py) | 75.6 | 75.1 | 70.2 | 44.5 | 93.4 | 79.4 | 81.7 | 99.0 | 77.4 ± 1.0 |
Requirements:
You will need to install poppler-utils and additional fonts for rendering PDF images.
Install dependencies (Ubuntu/Debian)
sudo apt-get update
sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools
Set up a conda environment and install olmocr. The requirements for running olmOCR are difficult to install in an existing python environment, so please do make a clean python environment to install into.
conda create -n olmocr python=3.11
conda activate olmocr
# For CPU-only operations, ex running the benchmark
pip install olmocr[bench]
# For actually converting the files with your own GPU
pip install olmocr[gpu] --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
For quick testing, try the web demo. To run locally, a GPU is required, as inference is powered by sglang under the hood.
Convert a Single PDF:
# Download a sample PDF
curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
# Convert it to markdown
python -m olmocr.pipeline ./localworkspace --markdown --pdfs olmocr-sample.pdf
Convert an Image file:
python -m olmocr.pipeline ./localworkspace --markdown --pdfs random_page.png
Convert Multiple PDFs:
python -m olmocr.pipeline ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*.pdf
With the addition of the --markdown flag, results will be stored as markdown files inside of ./localworkspace/markdown/.
The ./localworkspace/ workspace folder will then have both Dolma and markdown files (if using --markdown).
cat localworkspace/markdown/olmocr-sample.md
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
...
If you want to convert millions of PDFs, using multiple nodes running in parallel, then olmOCR supports reading your PDFs from AWS S3, and coordinating work using an AWS S3 output bucket.
For example, you can start this command on your first worker node, and it will set up a simple work queue in your AWS bucket and start converting PDFs.
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf
Now on any subsequent nodes, just run this and they will start grabbing items from the same workspace queue.
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace
If you are at Ai2 and want to linearize millions of PDFs efficiently using beaker, just add the --beaker
flag. This will prepare the workspace on your local machine, and then launch N GPU workers in the cluster to start
converting PDFs.
For example:
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf --beaker --beaker_gpus 4
Pull the Docker image.
docker pull alleninstituteforai/olmocr:latest
To run the container interactively:
docker run -it --gpus all --name olmocr_container alleninstituteforai/olmocr:latest /bin/bash
If you want to access your local files inside the container, use volume mounting:
docker run -it --gpus all \
-v /path/to/your/local/files:/local_files \
--name olmocr_container \
alleninstituteforai/olmocr:latest /bin/bash
All dependencies are already installed. Once you’re inside the container, you can run olmOCR commands. For example:
curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
python -m olmocr.pipeline ./localworkspace --markdown --pdfs olmocr-sample.pdf
You can also visit our Docker repository on Docker Hub.
python -m olmocr.pipeline --help
usage: pipeline.py [-h] [--pdfs PDFS] [--workspace_profile WORKSPACE_PROFILE] [--pdf_profile PDF_PROFILE] [--pages_per_group PAGES_PER_GROUP]
[--max_page_retries MAX_PAGE_RETRIES] [--max_page_error_rate MAX_PAGE_ERROR_RATE] [--workers WORKERS] [--apply_filter] [--stats] [--model MODEL]
[--model_max_context MODEL_MAX_CONTEXT] [--model_chat_template MODEL_CHAT_TEMPLATE] [--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM]
[--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN] [--beaker] [--beaker_workspace BEAKER_WORKSPACE] [--beaker_cluster BEAKER_CLUSTER]
[--beaker_gpus BEAKER_GPUS] [--beaker_priority BEAKER_PRIORITY]
workspace
Manager for running millions of PDFs through a batch inference pipeline
positional arguments:
workspace The filesystem path where work will be stored, can be a local folder, or an s3 path if coordinating work with many workers, s3://bucket/prefix/
options:
-h, --help show this help message and exit
--pdfs PDFS Path to add pdfs stored in s3 to the workspace, can be a glob path s3://bucket/prefix/*.pdf or path to file containing list of pdf paths
--workspace_profile WORKSPACE_PROFILE
S3 configuration profile for accessing the workspace
--pdf_profile PDF_PROFILE
S3 configuration profile for accessing the raw pdf documents
--pages_per_group PAGES_PER_GROUP
Aiming for this many pdf pages per work item group
--max_page_retries MAX_PAGE_RETRIES
Max number of times we will retry rendering a page
--max_page_error_rate MAX_PAGE_ERROR_RATE
Rate of allowable failed pages in a document, 1/250 by default
--workers WORKERS Number of workers to run at a time
--apply_filter Apply basic filtering to English pdfs which are not forms, and not likely seo spam
--stats Instead of running any job, reports some statistics about the current workspace
--markdown Also write natural text to markdown files preserving the folder structure of the input pdfs
--model MODEL List of paths where you can find the model to convert this pdf. You can specify several different paths here, and the script will try to use the
one which is fastest to access
--model_max_context MODEL_MAX_CONTEXT
Maximum context length that the model was fine tuned under
--model_chat_template MODEL_CHAT_TEMPLATE
Chat template to pass to sglang server
--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM
Dimension on longest side to use for rendering the pdf pages
--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN
Maximum amount of anchor text to use (characters)
--beaker Submit this job to beaker instead of running locally
--beaker_workspace BEAKER_WORKSPACE
Beaker workspace to submit to
--beaker_cluster BEAKER_CLUSTER
Beaker clusters you want to run on
--beaker_gpus BEAKER_GPUS
Number of gpu replicas to run
--beaker_priority BEAKER_PRIORITY
Beaker priority level for the job
There are some nice reusable pieces of the code that may be useful for your own projects:
olmOCR is developed and maintained by the AllenNLP team, backed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. To learn more about who specifically contributed to this codebase, see our contributors page.
olmOCR is licensed under Apache 2.0. A full copy of the license can be found on GitHub.
@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},
}
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