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Nougat: Neural Optical Understanding for Academic Documents

Lukas Blecher · Guillem Cucurull Preixens · Thomas Scialom · Robert Stojnic

Halle B #67
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Wed 8 May 7:30 a.m. PDT — 9:30 a.m. PDT


Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human- readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.

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