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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/](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](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8), boosts olmOCR-bench score by ~4 points using synthetic data and introduces RL training.
+ - August 13, 2025 - v0.3.0 - [New model release](https://huggingface.co/allenai/olmOCR-7B-0825-FP8), fixes auto-rotation detection, and hallucinations on blank documents.
+ - July 24, 2025 - v0.2.1 - [New model release](https://huggingface.co/allenai/olmOCR-7B-0725-FP8), scores 3 points higher on [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/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](https://github.com/allenai/olmocr/tree/main/olmocr/train), 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](#using-docker)
+ - May 19, 2025 - v0.1.68 - [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/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**](https://github.com/allenai/olmocr/tree/main/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):
+```bash
+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.
+
+```bash
+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:
+```bash
+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:
+```bash
+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:
+```bash
+pip install olmocr[beaker]
+```
+
+**Option 4: Benchmark Suite**
+
+For running the olmOCR benchmark suite:
+```bash
+pip install olmocr[bench]
+```
+
+**Combined Installation**
+
+You can combine multiple options:
+```bash
+# 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:
+```bash
+ulimit -n 65536
+```
+
+### Usage Examples
+
+For quick testing, try the [web demo](https://olmocr.allen.ai/).
+
+**Convert a Single PDF (Local GPU):**
+```bash
+# 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:**
+```bash
+olmocr ./localworkspace --markdown --pdfs random_page.png
+```
+
+**Convert Multiple PDFs:**
+```bash
+olmocr ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*.pdf
+```
+
+**Use Remote Inference Server:**
+```bash
+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](https://github.com/allenai/dolma) and markdown files (if using `--markdown`).
+
+
+```bash
+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:**
+```bash
+# Lightweight installation - no GPU dependencies needed
+pip install olmocr
+```
+
+**Using an External Server:**
+```bash
+# 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:**
+```bash
+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](https://ai2endpoints.cirrascale.ai/models/overview) | $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](https://deepinfra.com/) | $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](https://www.saas.parasail.io/serverless?name=olmocr-7b-1025-fp8) | $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:**
+```bash
+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:**
+```bash
+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](https://www.beaker.org), install with Beaker support:
+
+```bash
+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:
+
+```bash
+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):
+```bash
+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:
+```bash
+docker pull alleninstituteforai/olmocr:latest
+```
+
+#### Quick Start - Process PDFs
+
+Process a single PDF in your current directory:
+```bash
+docker run --gpus all \
+ -v $(pwd):/workspace \
+ alleninstituteforai/olmocr:latest-with-model \
+ -c "olmocr /workspace/output --markdown --pdfs /workspace/sample.pdf"
+```
+
+Process multiple PDFs:
+```bash
+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:
+```bash
+docker run -it --gpus all alleninstituteforai/olmocr:latest-with-model
+```
+
+> Visit our Docker repository on [Docker Hub](https://hub.docker.com/r/alleninstituteforai/olmocr) for more information.
+
+### Full Documentation
+
+To see all available options:
+```bash
+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](https://github.com/allenai/olmocr/blob/main/olmocr/data/buildsilver.py)
+ - Basic filtering by language and SEO spam removal - [filter.py](https://github.com/allenai/olmocr/blob/main/olmocr/filter/filter.py)
+ - SFT Finetuning code for Qwen2.5-VL - [train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/train.py)
+ - GRPO RL Trainer - [grpo_train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/grpo_train.py)
+ - Synthetic data generation - [mine_html_templates.py](https://github.com/allenai/olmocr/blob/main/olmocr/synth/mine_html_templates.py)
+ - Processing millions of PDFs through a finetuned model using VLLM - [pipeline.py](https://github.com/allenai/olmocr/blob/main/olmocr/pipeline.py)
+ - Viewing [Dolma docs](https://github.com/allenai/dolma) created from PDFs - [dolmaviewer.py](https://github.com/allenai/olmocr/blob/main/olmocr/viewer/dolmaviewer.py)
+
+
+
+## Team
+
+
+
+**olmOCR** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
+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](https://github.com/allenai/olmocr/graphs/contributors) page.
+
+
+
+## License
+
+
+
+**olmOCR** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+A full copy of the license can be found [on GitHub](https://github.com/allenai/olmocr/blob/main/LICENSE).
+
+
+
+## Citing
+
+For olmOCR v1 and OlmOCR-bench:
+```bibtex
+@misc{olmocrbench,
+ 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},
+}
+```
+
+For olmOCR v2 Unit Testing Rewards with RL:
+```bibtex
+@misc{olmocr2,
+ title={olmOCR 2: Unit Test Rewards for Document OCR},
+ author={Jake Poznanski and Luca Soldaini and Kyle Lo},
+ year={2025},
+ eprint={2510.19817},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV},
+ url={https://arxiv.org/abs/2510.19817},
+}
+```