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Maintainer: alaradirik

Total Score


Last updated 5/17/2024


Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

Nougat is a neural network model developed by alaradirik that focuses on understanding and extracting information from academic documents. It is designed to work with scanned PDFs or image files, converting them into a structured format that can be more easily processed and analyzed. Nougat builds upon similar models like text-extract-ocr, bunny-phi-2-siglip, and owlvit-base-patch32, which also target document understanding and processing tasks.

Model inputs and outputs

Nougat takes a scanned PDF or image file as input and outputs a structured representation of the document's content. This can include extracting the full text, identifying key sections or elements (e.g., titles, abstracts, figures, tables), and potentially even generating a summary or outline of the document.


  • Document: Scanned PDF or image file to convert


  • Output: Structured representation of the document's content


Nougat is designed to assist researchers, students, and professionals working with academic documents by automating the process of understanding and extracting information from these materials. It can help streamline tasks such as literature reviews, meta-analyses, and systematic reviews by quickly processing large collections of papers and surfacing the most relevant information.

What can I use it for?

Nougat could be particularly useful for academics, researchers, and knowledge workers who need to regularly process and analyze large volumes of scholarly literature. By automating the conversion of scanned PDFs into structured data, Nougat can save time and effort, allowing users to focus on higher-level analysis and synthesis tasks. It could also be integrated into document management systems or bibliographic software to enhance productivity and research workflows.

Things to try

One interesting aspect of Nougat is its ability to handle a wide range of document types and formats, from traditional journal articles to more diverse academic materials like conference proceedings, technical reports, and even handwritten notes. Users could experiment with feeding Nougat a variety of document sources and compare the quality and consistency of the output to understand the model's strengths and limitations. Additionally, exploring the level of detail and structure that Nougat can extract from documents could lead to novel applications and use cases.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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