Nougat: Neural Optical Understanding for Academic Documents
This work addresses the accessibility of scientific knowledge by bridging human-readable documents and machine-readable text, though it appears incremental as it builds on existing OCR and transformer methods.
The authors tackled the problem of semantic information loss in scientific PDFs, particularly for mathematical expressions, by proposing Nougat, a Visual Transformer model that converts scientific documents into markup language, demonstrating its effectiveness on a new dataset.
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.