olmocr

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

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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 in