With the growing advancements in the field of Artificial Intelligence, its sub-fields, including Natural Language Processing, Natural Language Generation, Computer Vision, etc., have rapidly gained a lot of popularity due to their extensive use cases. Optical Character Recognition (OCR) is a well-established and heavily investigated area of computer vision. It has a number of uses, such as document digitization, handwriting recognition, and scene text identification. The recognition of mathematical expressions is one area of OCR that has received a lot of interest in academic studies.

The Portable Document Format (PDF) is one of the most widely used formats for scientific knowledge, which is often preserved in books or published in scholarly journals. The second most used data format on the internet, accounting for 2.4% of the information, PDFs are frequently used for document delivery. Despite their widespread use, extracting information from PDF files can be difficult, particularly when dealing with highly specialized materials like scientific research articles. In particular, when these papers are converted to PDF format, the semantic information of mathematical expressions is frequently lost.

To address the challenges, a team of researchers from Meta AI has introduced a solution called Nougat, which stands for “Neural Optical Understanding for Academic Documents.” In order to do Optical Character Recognition (OCR) on scientific texts, Nougat is a Visual Transformer model. Its goal is to transform these files into a markup language so that they may be more easily accessed and machine-readable.

To show the efficacy of the methodology, the team has also produced a fresh dataset of academic papers. This method offers a viable answer for enhancing scientific knowledge accessibility in the digital age. It fills the gap between written materials that are simple for people to read and text that computers can process and analyze. Researchers, educators, and anyone interested in scientific literature can access and deal with scientific papers more effectively using Nougat. Nougat is basically a transformer-based model designed to convert images of document pages, particularly those from PDFs, into formatted markup text.

The team has summarized their key contributions as follows –

Publication of a Pre-trained Model: The team has created a pre-trained model that can transform PDFs into a simple markup language. This pre-trained model is made public on GitHub, where the research community and anyone can access it, along with the related code.

Pipeline for Dataset Creation: A method for building datasets that pair PDF documents with their associated source code is described in the study. This dataset development method is crucial for testing and refining the Nougat model and may be useful for future document analysis research and applications.

Dependency on the Page’s Image Only: One of Nougat’s standout features is its capacity to operate only on the Page’s Image. This makes it a flexible tool for extracting content from a variety of sources, even when the original documents are not available in digital text formats. It can process scanned papers and books.

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