Nougat: Neural Optical Understanding for Academic Documents

What is the project about?

Nougat is an academic document PDF parser that can understand LaTeX math and tables, converting them into a lightweight markup language. It's designed to extract text and structured information from scientific documents.

What problem does it solve?

Nougat addresses the challenge of accurately extracting information from scientific PDFs, which often contain complex mathematical equations and tables that are difficult for standard OCR tools to process. It eliminates the need for manual conversion or specialized tools that may not handle scientific notation well.

What are the features of the project?

PDF Parsing: Converts academic PDFs into a lightweight markup language (.mmd).
LaTeX Understanding: Accurately recognizes and processes mathematical equations and tables written in LaTeX.
Command-Line Interface (CLI): Provides a simple way to process PDFs from the command line.
API: Offers an API for integrating Nougat into other applications.
Dataset Generation: Includes tools for creating datasets for training and evaluation.
Batch Processing: Can process multiple PDFs at once.
Page Selection: Allows processing of specific page ranges within a PDF.
Markdown Compatibility: Post-processing for Mathpix Markdown compatibility.
Failure Detection Heuristic: Attempts to identify and flag pages that could not be processed correctly (can be disabled).
Train and Evaluate: Capable of training or fine tuning a Nougat model, and evaluating the model.

What are the technologies used in the project?

Python: The primary programming language.
PyTorch: Deep learning framework.
Donut: The project builds upon the Donut framework.
LaTeXML: Used for processing .tex files to .html for dataset generation.
pdffigures2: Used for extracting figures from PDFs during dataset generation.
Tesseract OCR (optional): Can be used for additional OCR prediction during dataset generation.

What are the benefits of the project?

Accurate Information Extraction: Provides more accurate extraction of text, math, and tables from scientific documents compared to standard OCR.
Automation: Automates the process of converting PDFs to a structured format.
Open Source: The codebase is open source (MIT license), and the model weights are available (CC-BY-NC license).
Extensible: Can be integrated into other workflows and applications via the API.
Research Enablement: Facilitates research that requires processing large numbers of scientific papers.

What are the use cases of the project?

Academic Research: Extracting data and information from research papers for literature reviews, meta-analyses, and other research tasks.
Data Mining: Building datasets of scientific information from PDFs.
Document Conversion: Converting scientific papers into more accessible formats.
Information Retrieval: Improving search and indexing of scientific documents.
Automated Content Generation: Creating summaries or other content based on the extracted information.