invoice-and-receipts_donut_v1
| Property | Value |
|---|---|
| Parameter Count | 202M |
| License | Apache-2.0 |
| Author | mychen76 |
| Model Type | Vision-Encoder-Decoder |
| Framework | PyTorch |
What is invoice-and-receipts_donut_v1?
invoice-and-receipts_donut_v1 is a specialized vision-encoder-decoder model designed to transform invoice and receipt images directly into structured data formats (JSON or XML) without requiring a separate OCR engine. Built upon the Donut architecture, this model represents a significant advancement in document processing automation.
Implementation Details
The model leverages a transformer-based architecture optimized for image-to-text conversion, utilizing 202M parameters to achieve high accuracy in document parsing. It employs safetensors for efficient model weight storage and supports inference endpoints for practical deployment.
- Direct image-to-structured-data conversion without OCR dependency
- Support for both JSON and XML output formats
- Comprehensive extraction of invoice details including header information, line items, and summaries
- Efficient processing with reduced resource utilization
Core Capabilities
- Extraction of invoice metadata (invoice numbers, dates, tax IDs)
- Detailed line item parsing including quantities, prices, and VAT calculations
- Automatic generation of structured summaries with totals
- Support for complex document layouts and varying formats
Frequently Asked Questions
Q: What makes this model unique?
This model's key differentiator is its ability to process invoices and receipts without requiring a separate OCR engine, leading to improved performance and reduced deployment complexity.
Q: What are the recommended use cases?
The model is ideal for automated invoice processing systems, accounting software integration, expense management systems, and any application requiring structured data extraction from invoice images.
