Donut Base Fine-tuned DocVQA
| Property | Value |
|---|---|
| License | MIT |
| Paper | OCR-free Document Understanding Transformer |
| Developer | Naver Clova IX |
| Downloads | 17,519 |
What is donut-base-finetuned-docvqa?
Donut is an innovative document understanding transformer that operates without traditional OCR (Optical Character Recognition). This particular model is fine-tuned specifically for document visual question answering tasks using the DocVQA dataset. It represents a significant advancement in document processing by directly understanding document contents through a transformer-based architecture.
Implementation Details
The model employs a dual-architecture approach, combining a Swin Transformer as the vision encoder with a BART text decoder. The vision encoder processes document images into embedded representations, while the decoder generates text responses autoregressively based on these encodings. This architecture enables end-to-end document understanding without intermediate OCR steps.
- Vision Encoder: Swin Transformer for image processing
- Text Decoder: BART for text generation
- Fine-tuned specifically for document question-answering tasks
- Supports inference endpoints for practical deployment
Core Capabilities
- Document visual question answering
- OCR-free document understanding
- Image-to-text generation
- Flexible document processing across various formats
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its OCR-free approach to document understanding, making it more efficient and potentially more accurate than traditional OCR-based systems. The combination of Swin Transformer and BART creates a powerful end-to-end solution for document processing.
Q: What are the recommended use cases?
The model is particularly well-suited for document question answering tasks, such as extracting specific information from invoices, contracts, and other business documents. It can handle various document formats and answer questions about document content without requiring separate OCR processing.
