Noureddine RAMDI / Nougat: Vision Transformer OCR for academic PDFs extracting LaTeX math and tables

Created Sat, 23 May 2026 20:41:14 +0000 Modified Sat, 23 May 2026 20:41:27 +0000
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github-stars python ocr vision-transformer academic-pdfs latex machine-learning

facebookresearch/nougat

Nougat tackles a specific pain point that many OCR systems struggle with: accurately extracting LaTeX math formulas and tables from academic PDFs. Instead of the traditional OCR-then-paste approach, Nougat processes rendered PDF pages as images and directly generates markup text. This approach improves structured extraction of complex academic content, a known challenge in document understanding.

Nougat’s vision transformer approach to PDF OCR

Nougat is Meta’s neural optical character recognition system tailored for academic PDFs. Its standout feature is the ability to parse LaTeX math and tables, converting them into Mathpix-compatible Markdown. This is particularly useful for researchers and academics who need to convert PDFs into editable, structured formats.

Under the hood, Nougat uses a Vision Transformer (ViT) encoder-decoder architecture adapted from the Donut framework. The input to the model is rendered PDF pages treated as images. The model then autoregressively generates markup text, essentially “translating” visual content into structured markup.

The system ships with two model variants: the default 0.1.0-small and a higher-accuracy 0.1.0-base. This allows users to balance inference speed and accuracy depending on their needs.

Nougat also integrates a failure detection heuristic to skip pages it cannot reliably parse, which helps avoid noisy or incorrect outputs on difficult pages.

The repo includes several components beyond the model itself:

  • A command-line interface (CLI) for batch processing PDFs.
  • An HTTP API server to integrate the OCR model into other applications.
  • Dataset generation tools that pair PDFs with LaTeXML-processed ground truth, facilitating fine-tuning.
  • A full training pipeline for customizing the model on domain-specific documents.

Technical strengths and design tradeoffs

Nougat’s key technical strength lies in its end-to-end vision transformer approach, which bypasses the common OCR pipeline of text detection followed by recognition and post-processing. By autoregressively generating markup directly from images, it can better capture the structure and semantics of complex academic content like LaTeX math and tables.

The use of the Donut framework as a base is notable because Donut is designed for document understanding tasks with an encoder-decoder ViT architecture. Nougat adapts this to the academic PDF domain, addressing the unique challenges such as precise math symbol recognition and table structure extraction.

The failure detection heuristic is a practical addition to ensure quality. Instead of forcing predictions on every page, it identifies pages where the model is uncertain and skips them, reducing false positives and improving overall output reliability.

There is a tradeoff in model size and accuracy: the smaller model is faster but less accurate, while the base model improves accuracy at the cost of increased resource usage. Users must choose based on their deployment constraints.

From a code quality perspective, the repo is well organized, with clear separation between model code, dataset generation, and serving components. The training pipeline and dataset tools demonstrate good practices for extensibility and reproducibility.

One limitation is that the system is specialized for academic PDFs containing LaTeX math and tables. It may not perform well on general documents or scanned images without clear rendering. Also, the autoregressive generation approach can be slower than traditional OCR pipelines for large volumes.

Quick start

Installation from pip is straightforward:

pip install nougat-ocr

Alternatively, install directly from the repository:

pip install git+https://github.com/facebookresearch/nougat

For API or dataset functionality, install with extras:

pip install "nougat-ocr[api]"

or

pip install "nougat-ocr[dataset]"

To run predictions on a PDF with the CLI:

$ nougat path/to/file.pdf -o output_directory

You can also process an entire directory or a file listing multiple PDFs:

$ nougat path/to/directory -o output_directory

Command line options include batch size, checkpoint selection, model variant, and page range selection, among others.

Verdict

Nougat is a solid tool for academic users and researchers who need to extract LaTeX math and tables from PDFs into editable Markdown formats. Its ViT encoder-decoder architecture and autoregressive markup generation set it apart from traditional OCR pipelines, offering better handling of structured academic content.

However, it is specialized and may not be suitable for general document OCR tasks or scanned documents without clear rendering. The tradeoff between model size and accuracy is something to consider for deployment.

Its inclusion of a CLI, API server, dataset generation, and training pipeline makes it a comprehensive toolkit for research and customization. If your use case involves academic PDFs and you need structured output beyond plain text, Nougat is worth evaluating.

→ GitHub Repo: facebookresearch/nougat ⭐ 9,977 · Python