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:
- Convert PDF, PNG, and JPEG based documents into clean Markdown
- Support for equations, tables, handwriting, and complex formatting
- Automatically removes headers and footers
- Convert into text with a natural reading order, even in the presence of
figures, multi-column layouts, and insets
- Efficient, less than $200 USD per million pages converted
- (Based on a 7B parameter VLM, so it requires a GPU)
News
- October 21, 2025 - v0.4.0 - New model release, boosts olmOCR-bench score by ~4 points using synthetic data and introduces RL training.
- August 13, 2025 - v0.3.0 - New model release, fixes auto-rotation detection, and hallucinations on blank documents.
- July 24, 2025 - v0.2.1 - New model release, scores 3 points higher on olmOCR-Bench, also runs significantly faster because it's default FP8, and needs much fewer retries per document.
- July 23, 2025 - v0.2.0 - New cleaned up trainer code, makes it much simpler to train olmOCR models yourself.
- 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
- May 19, 2025 - v0.1.68 - olmOCR-Bench launch, scoring 77.4. Launch includes 2 point performance boost in olmOCR pipeline due to bug fixes with prompts.
- Mar 17, 2025 - v0.1.60 - Performance improvements due to better temperature selection in sampling.
- Feb 25, 2025 - v0.1.58 - Initial public launch and demo.
Benchmark
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.
|
ArXiv |
Old
scans
math |
Tables |
Old
scans |
Headers
&
footers |
Multi
column |
Long
tiny
text |
Base |
Overall |
| Mistral OCR API |
77.2 |
67.5 |
60.6 |
29.3 |
93.6 |
71.3 |
77.1 |
99.4 |
72.0±1.1 |
| Marker 1.10.1 |
83.8 |
66.8 |
72.9 |
33.5 |
86.6 |
80.0 |
85.7 |
99.3 |
76.1±1.1 |
| MinerU 2.5.4* |
76.6 |
54.6 |
84.9 |
33.7 |
96.6 |
78.2 |
83.5 |
93.7 |
75.2±1.1 |
| DeepSeek-OCR |
77.2 |
73.6 |
80.2 |
33.3 |
96.1 |
66.4 |
79.4 |
99.8 |
75.7±1.0 |
| Nanonets-OCR2-3B |
75.4 |
46.1 |
86.8 |
40.9 |
32.1 |
81.9 |
93.0 |
99.6 |
69.5±1.1 |
| PaddleOCR-VL* |
85.7 |
71.0 |
84.1 |
37.8 |
97.0 |
79.9 |
85.7 |
98.5 |
80.0±1.0 |
| Infinity-Parser 7B* |
84.4 |
83.8 |
85.0 |
47.9 |
88.7 |
84.2 |
86.4 |
99.8 |
82.5±? |
| Chandra OCR 0.1.0* |
82.2 |
80.3 |
88.0 |
50.4 |
90.8 |
81.2 |
92.3 |
99.9 |
83.1±0.9 |
|
| olmOCR v0.4.0 |
83.0 |
82.3 |
84.9 |
47.7 |
96.1 |
83.7 |
81.9 |
99.7 |
82.4±1.1 |
Installation
System Dependencies
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
Python Installation
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
Choose the installation option that matches your use case:
Option 1: Remote Inference (Lightweight)
If you plan to use a remote vLLM server with the --server flag, install the base package:
pip install olmocr
This avoids installing heavy GPU dependencies like PyTorch (~2GB+).
Option 2: Local GPU Inference
Requirements:
- Recent NVIDIA GPU (tested on RTX 4090, L40S, A100, H100) with at least 12 GB of GPU RAM
- 30GB of free disk space
For running inference with your own GPU:
pip install olmocr[gpu] --extra-index-url https://download.pytorch.org/whl/cu128
# Recommended: Install flash infer for faster inference on GPU
pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
Option 3: Beaker Cluster Execution
For submitting jobs to Beaker clusters with the --beaker flag:
pip install olmocr[beaker]
Option 4: Benchmark Suite
For running the olmOCR benchmark suite:
pip install olmocr[bench]
Combined Installation
You can combine multiple options:
# GPU + Beaker support
pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
# GPU + Benchmark support
pip install olmocr[gpu,bench] --extra-index-url https://download.pytorch.org/whl/cu128
Troubleshooting
If you run into errors about too many open files, update your ulimit:
ulimit -n 65536
Usage Examples
For quick testing, try the web demo.
Convert a Single PDF (Local GPU):
# Download a sample PDF
curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
# Convert it to markdown
olmocr ./localworkspace --markdown --pdfs olmocr-sample.pdf
Convert an Image file:
olmocr ./localworkspace --markdown --pdfs random_page.png
Convert Multiple PDFs:
olmocr ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*.pdf
Use Remote Inference Server:
olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs *.pdf
With the --markdown flag, results will be stored as markdown files inside of ./localworkspace/markdown/.
Note: You can also use python -m olmocr.pipeline instead of olmocr if you prefer.
Viewing Results
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
...
Using an Inference Provider or External Server
If you have a vLLM server already running elsewhere (or any inference platform implementing the OpenAI API), you can point olmOCR to use it instead of spawning a local instance.
Installation for Remote Inference:
# Lightweight installation - no GPU dependencies needed
pip install olmocr
Using an External Server:
# Use external vLLM server instead of local one
olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs tests/gnarly_pdfs/*.pdf
The served model name in vLLM needs to match the value provided in --model.
Example vLLM Server Launch:
vllm serve allenai/olmOCR-2-7B-1025-FP8 --max-model-len 16384
Verified External Providers
We have tested olmOCR-2-7B-1025-FP8 on these external model providers and confirmed that they work
|
$/1M Input tokens |
$/1M Output tokens |
Example Command |
| Cirrascale |
$0.07 |
$0.15 |
olmocr ./workspace --server https://ai2endpoints.cirrascale.ai/api --api_key sk-XXXXXXX --workers 1 --max_concurrent_requests 20 --model olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf |
| DeepInfra |
$0.09 |
$0.19 |
olmocr ./workspace --server https://api.deepinfra.com/v1/openai --api_key DfXXXXXXX --workers 1 --max_concurrent_requests 20 --model allenai/olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf |
| Parasail |
$0.10 |
$0.20 |
olmocr ./workspace --server https://api.parasail.io/v1 --api_key psk-XXXXX --workers 1 --max_concurrent_requests 20 --model allenai/olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf |
Notes on arguments
--server: Defines the OpenAI-compatible endpoint: ex https://api.deepinfra.com/v1/openai--api_key: Your API key, bassed in via Authorization Bearer HTTP header--max_concurrent_requests: Max concurrent requests that will be in-flight to the inference provider at one time--workers: Max number of page groups that will be processed at once. You may want to set this to 1 so that you finish one group of stuff before moving on.--pages_per_group: You may want a smaller number of pages per group as many external provides have lower concurrent request limits--model: The model identifier, ex. allenai/olmOCR-2-7B-1025, different providers have different names, and if you run locally, you can use olmocr- Other arguments work the same as with local inference
Multi-node / Cluster Usage
If you want to convert millions of PDFs using multiple nodes running in parallel, olmOCR supports
reading PDFs from AWS S3 and coordinating work using an AWS S3 output bucket.
Start the first worker node:
olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf
This sets up a simple work queue in your AWS bucket and starts converting PDFs.
On subsequent worker nodes:
olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace
They will automatically start grabbing items from the same workspace queue.
Using Beaker for Cluster Execution
If you are at Ai2 and want to linearize millions of PDFs efficiently using beaker, install with Beaker support:
pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
Then use the --beaker flag to prepare the workspace locally and launch N GPU workers in the cluster:
olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf --beaker --beaker_gpus 4
Using Docker
Pull the Docker image (large, includes the model, ~30GB):
docker pull alleninstituteforai/olmocr:latest-with-model
For advanced users who want to manage their own model downloads, we also provide a base image without the model:
docker pull alleninstituteforai/olmocr:latest
Quick Start - Process PDFs
Process a single PDF in your current directory:
docker run --gpus all \
-v $(pwd):/workspace \
alleninstituteforai/olmocr:latest-with-model \
-c "olmocr /workspace/output --markdown --pdfs /workspace/sample.pdf"
Process multiple PDFs:
docker run --gpus all \
-v /path/to/pdfs:/input \
-v /path/to/output:/output \
alleninstituteforai/olmocr:latest-with-model \
-c "olmocr /output --markdown --pdfs /input/*.pdf"
Interactive Mode
Run the container interactively for exploration and debugging:
docker run -it --gpus all alleninstituteforai/olmocr:latest-with-model
Visit our Docker repository on Docker Hub for more information.
Full Documentation
To see all available options:
olmocr --help
usage: pipeline.py [-h] [--pdfs [PDFS ...]] [--model MODEL] [--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] [--markdown] [--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM] [--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN] [--guided_decoding] [--gpu-memory-utilization GPU_MEMORY_UTILIZATION] [--max_model_len MAX_MODEL_LEN]
[--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--data-parallel-size DATA_PARALLEL_SIZE] [--port PORT] [--server SERVER] [--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
--model MODEL Path where the model is located, allenai/olmOCR-7B-0725-FP8 is the default, can be local, s3, or hugging face.
--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
--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), not used for new models
--guided_decoding Enable guided decoding for model YAML type outputs
VLLM arguments:
--gpu-memory-utilization GPU_MEMORY_UTILIZATION
Fraction of VRAM vLLM may pre-allocate for KV-cache (passed through to vllm serve).
--max_model_len MAX_MODEL_LEN
Upper bound (tokens) vLLM will allocate KV-cache for, lower if VLLM won't start
--tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
Tensor parallel size for vLLM
--data-parallel-size DATA_PARALLEL_SIZE, -dp DATA_PARALLEL_SIZE
Data parallel size for vLLM
--port PORT Port to use for the VLLM server
--server SERVER URL of external vLLM (or other compatible provider)
server (e.g., http://hostname:port). If provided,
skips spawning local vLLM instance
beaker/cluster execution:
--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
Code overview
There are some nice reusable pieces of the code that may be useful for your own projects:
- A prompting strategy to get really good natural text parsing using ChatGPT 4o - buildsilver.py
- Basic filtering by language and SEO spam removal - [filter.py]
olmOCR — это инструментарий, который превращает PDF, PNG и JPEG в аккуратный Markdown или простой текст с правильным порядком чтения, сохраняя формулы, таблицы и рукописный текст. Создан в первую очередь для получения обучающих датасетов для LLM.