Curator's take
Reach for olmocr when you have lots of messy PDFs to convert into high-quality text for a training corpus or RAG index and you have GPU to run the VLM. It is the parsing FRONT-END of a pipeline, not a RAG system itself — pair it with an indexer/retriever (LlamaIndex) and a vector store (Chroma). NOT worth it for clean, digital-native PDFs where a cheap text extractor does the job — running a vision-language model for those is overkill.
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
Continue your stackWhat teams reach for next — and why each earns a place beside olmocr. Pairs well with LlamaIndex Data framework for connecting custom data sources to LLMs — ingestion, indexing, retrieval. Why it fitsolmocr is the document-parsing front-end; LlamaIndex is the indexing/retrieval layer. Run messy PDFs through olmocr to get clean text, then hand that text to LlamaIndex to chunk, embed, and retrieve — a natural two-stage RAG-ingestion pipeline. Chroma Open-source embedding database for building AI apps with retrieval. Why it fitsolmocr's cleaned text is exactly what you embed and store for retrieval — feed its output into Chroma as the vector store behind a RAG app. Looser than the LlamaIndex pairing since any embedder/DB works, but Chroma is the natural open-source landing spot. |