New updates for olmOCR, our fully open toolkit for transforming documents (PDFs & images) into clean markdown. We released:
1️⃣ New benchmark for fair comparison of OCR engines and APIs
2️⃣ Improved inference that is faster and cheaper to run
3️⃣ Docker image for easy deployment
Most OCR benchmarks compare model output to a fixed reference text. This approach can be misleading, since it penalizes correct outputs that differ in style only.
For olmOCR-Bench, we created 7000+ unit tests over 1400+ documents to test core extraction capabilities:
For olmOCR-Bench, we created 7000+ unit tests over 1400+ documents to test core extraction capabilities:
Our tests check if math equations are transcribed, tables contain the right values, boilerplate is removed, etc. Each one is designed to be simple, unambiguous, and machine-verifiable.
On these rigorous tests, olmOCR outperforms all other models we compared to:
On these rigorous tests, olmOCR outperforms all other models we compared to:
olmOCR is also now easier to use than ever:
- Simpler installation
- Prebuilt Docker containers
- Upgraded to the latest vLLM version (support for quantization coming soon!)
- Better performance with improved sampling, tweaked retry strategy, and a cleaner prompt!
- Simpler installation
- Prebuilt Docker containers
- Upgraded to the latest vLLM version (support for quantization coming soon!)
- Better performance with improved sampling, tweaked retry strategy, and a cleaner prompt!
Run olmOCR-bench yourself: github.com/allenai/olmocr…
OCR your own documents: github.com/allenai/olmocr
Try the olmOCR online demo: olmocr.allenai.org
Read our updated technical report: olmocr.allenai.org/papers/olmocr.…
OCR your own documents: github.com/allenai/olmocr
Try the olmOCR online demo: olmocr.allenai.org
Read our updated technical report: olmocr.allenai.org/papers/olmocr.…
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