🔥 olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

💡 The paper presents olmOCR, an open source toolkit that uses a fine tuned vision language model to extract clean text from PDF documents while preserving their structure. The problem addressed is that PDFs come in diverse formats and visual layouts, making it challenging to extract and represent their content for language model use. The method involves using a 7 billion parameter vision language model trained on a sample of 260,000 pages from over 100,000 crawled PDFs with diverse properties. The model is fine tuned to process PDFs into clean linearized plain text in natural reading order, preserving structured content such as sections, tables, lists, and equations. The results show that olmOCR is optimized for large scale batch processing, able to scale flexibly to different hardware setups, and can convert a million PDF pages for a relatively low cost of 190 USD. The toolkit is released as open source, including the vision language model weights, data, training code, and inference code, making it accessible for use in training language models with the trillions of tokens available in PDF documents. Overall, the paper contributes a scalable and cost effective solution for unlocking the content of PDF documents, which can be used to train high quality language models.

📅 Published on Feb 25, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Project Page: https://olmocr.allenai.org/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/allenai/olmOCR-bench
• https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered
• https://huggingface.co/datasets/Voxel51/olmOCR_bench

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/davanstrien/benchmark-race
• https://huggingface.co/spaces/OpenEvals/every-leaderboards
• https://huggingface.co/spaces/OpenEvals/leaderboard-watcher

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageModels #PDFTextExtraction #DocumentLayoutAnalysis #OCRTechniques #NaturalLanguageProcessing