OCR-Donut-CORD
| Property | Value |
|---|---|
| License | MIT |
| Paper | OCR-free Document Understanding Transformer |
| Downloads | 1,734 |
| Architecture | Vision-encoder-decoder (Swin Transformer + BART) |
What is OCR-Donut-CORD?
OCR-Donut-CORD is an innovative document understanding model that processes documents without traditional OCR. It combines a Swin Transformer vision encoder with a BART text decoder, specifically fine-tuned on the CORD (Consolidated Receipt Dataset) for parsing receipts and similar documents.
Implementation Details
The model architecture consists of two main components: a vision encoder using Swin Transformer technology to process document images, and a text decoder based on BART that generates text output. This combination enables direct document understanding without intermediate OCR steps.
- Vision Encoder: Swin Transformer processes image inputs into embedded representations
- Text Decoder: BART generates text autoregressively based on encoded image features
- Fine-tuned specifically for receipt parsing on CORD dataset
Core Capabilities
- OCR-free document parsing and understanding
- Receipt information extraction and structuring
- End-to-end document processing without intermediate steps
- Automated information extraction from visual documents
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its OCR-free approach to document understanding, eliminating the need for traditional character recognition steps while maintaining high accuracy in document parsing.
Q: What are the recommended use cases?
The model is specifically designed for parsing receipts and similar structured documents, making it ideal for retail analytics, expense management systems, and automated document processing pipelines.
