Nougat: Neural Optical Understanding for Academic Documents

Code

• github.com/facebookresearch/nougat
  OfficialIn paperpytorch★ 9,871
• github.com/huggingface/transformers
  pytorch★ 158,292
• github.com/pwc-1/Paper-9/tree/main/2/nougat
  mindspore★ 0

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.

Tasks

Reproductions