Nougat: Neural Optical Understanding for Academic Documents
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2023
- DOI:
- 10.48550/arXiv.2308.13418
- arXiv:
- arXiv:2308.13418
- Bibcode:
- 2023arXiv230813418B
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- 17 pages, 10 figures