Donut Base Model
| Property | Value |
|---|---|
| License | MIT |
| Paper | OCR-free Document Understanding Transformer |
| Downloads | 42,138 |
| Tags | Image-to-Text, Vision, Transformers |
What is donut-base?
Donut-base is a groundbreaking document understanding transformer model developed by Naver Clova IX that processes documents without traditional OCR methods. It leverages a unique architecture combining a Swin Transformer for vision encoding and BART for text decoding, enabling direct document understanding and text generation from images.
Implementation Details
The model employs a two-stage architecture where the vision encoder processes document images into embedded representations, which are then decoded into text through an autoregressive decoder. This approach eliminates the need for intermediate OCR processing, potentially reducing errors and improving efficiency.
- Vision Encoder: Swin Transformer architecture for image processing
- Text Decoder: BART-based autoregressive text generation
- OCR-free approach for direct document understanding
Core Capabilities
- Document image processing and understanding
- Text extraction without OCR
- Flexible fine-tuning for various document processing tasks
- Support for document classification and parsing
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to process documents without OCR, using an end-to-end transformer architecture that directly converts document images to text understanding. This approach potentially reduces errors associated with traditional OCR pipelines.
Q: What are the recommended use cases?
The base model is designed to be fine-tuned for specific document processing tasks such as document classification, information extraction, and document parsing. It serves as a foundation for developing specialized document understanding applications.
