O C R Donut C O R D

Introduction

The Donut model is a fine-tuned version specifically adjusted for the CORD dataset, which is used for document parsing. It combines a vision encoder with a text decoder to perform OCR-free document understanding. This model was introduced in the paper "OCR-free Document Understanding Transformer" by Geewok et al.

Architecture

The Donut model is composed of two main components:

• Vision Encoder: Utilizes the Swin Transformer to encode images into tensors of embeddings.
• Text Decoder: Employs BART to generate text autoregressively, based on the encoded image data.

Training

The model is trained on the CORD dataset, which is a consolidated receipt dataset used for post-OCR parsing tasks. It is designed to generate text outputs directly from image inputs without requiring explicit OCR steps.

Guide: Running Locally

1. Setup Environment

   • Install dependencies using pip install transformers torch.
   • Clone the repository containing the Donut model.

2. Loading the Model

   • Load the model and tokenizer using the transformers library.

3. Inference

   • Prepare the input image and process it through the model.
   • Decode the output to generate text from the image data.

4. Cloud GPUs

   • Leverage cloud services such as AWS, Google Cloud, or Azure for GPU resources to handle intensive computations efficiently.

License

The Donut model is released under the MIT License, allowing for open and flexible use in various applications.