Donut

Donut (Document Understanding Transformer) is an OCR-free document understanding framework that directly extracts structured information from document images without relying on traditional OCR engines. Built on Transformer-based deep learning models, Donut supports document classification, information extraction, receipt parsing, invoice processing, visual question answering (VQA), and intelligent document automation. This pre-configured Ubuntu 26.04 LTS environment includes Donut, Python 3, and the required AI libraries for document processing and machine learning workflows.

Features of Donut:

• Provides OCR-free document understanding using Transformer-based AI models.
• Supports invoice, receipt, and form data extraction workflows.
• Generates structured JSON outputs directly from document images.
• Enables document classification and information extraction tasks.
• Supports Visual Question Answering (VQA) on document content.
• Includes pre-trained models for rapid deployment and experimentation.
• Supports custom fine-tuning for domain-specific document datasets.
• Built on PyTorch and Hugging Face Transformers for scalable AI workloads.
• Supports CPU and GPU-based inference and model training.
• Includes Donut, Python 3, PyTorch, Transformers, and related dependencies on Ubuntu 26.04 LTS.

Donut usage:

$ sudo su
$ cd /opt
$ source donut-env/bin/activate
$ pip show donut-python

Disclaimer: Donut is an open-source document AI framework developed by the Donut project contributors and community. It provides machine learning models and tools for document understanding and information extraction. Users are responsible for configuring, securing, validating, maintaining, and monitoring their document processing workflows, models, datasets, and integrations according to their