Nougat: Neural Optical Understanding for Academic Documents

08/25/2023
by   Lukas Blecher, et al.
0

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.

READ FULL TEXT
05/27/2019

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

In this paper, we present a new dataset for Form Understanding in Noisy ...
07/02/2021

Optical Braille Recognition using Circular Hough Transform

Braille has empowered visually challenged community to read and write. B...
12/20/2018

Automatic Quality Assurance and Release (Report from Dagstuhl Seminar 18122)

This report documents the program and the outcomes of Dagstuhl Seminar 1...
04/06/2018

Extracting Scientific Figures with Distantly Supervised Neural Networks

Non-textual components such as charts, diagrams and tables provide key i...
07/05/2022

Keyword Extraction in Scientific Documents

The scientific publication output grows exponentially. Therefore, it is ...
05/10/2018

hyperdoc2vec: Distributed Representations of Hypertext Documents

Hypertext documents, such as web pages and academic papers, are of great...
01/28/2022

Automated Creation and Human-assisted Curation of Computable Scientific Models from Code and Text

Scientific models hold the key to better understanding and predicting th...

Please sign up or login with your details

Forgot password? Click here to reset