---
language: en
license: mit
tags:
- vision
- document-ai
- donut
- ocr-free
- image-to-text
---

# document_parsing_donut_v1

## Overview
This model is an implementation of the **Donut** (Document Understanding Transformer) architecture. Unlike traditional OCR-based systems, this model is OCR-free, meaning it maps raw document images directly to structured JSON outputs. It is fine-tuned to parse complex layouts such as invoices, receipts, and technical forms without a separate text recognition step.



## Model Architecture
The model utilizes a vision-encoder text-decoder framework:
- **Encoder**: A Swin Transformer that processes high-resolution images into visual features.
- **Decoder**: A BART-based multi-lingual transformer that generates text tokens in a sequence-to-sequence manner.
- **Objective**: The model is trained using a cross-entropy loss to predict the next token based on both the visual input and preceding tokens:
$$\mathcal{L} = -\sum_{t=1}^{T} \log P(y_t | y_{<t}, \mathbf{x})$$

## Intended Use
- **Automated Data Entry**: Extracting key-value pairs from digitized business documents.
- **Layout Analysis**: Identifying structural components (headers, tables, footers) in multi-page PDFs.
- **Archival Digitization**: Converting historical scanned documents into searchable, structured data.

## Limitations
- **Resolution Sensitivity**: Performance drops significantly if images are scaled below 960x1280 pixels.
- **Language Bias**: While capable, accuracy is highest for Latin-script documents; CJK and Arabic scripts require specialized fine-tuning.
- **Handwriting**: The model is optimized for printed text and may struggle with highly cursive or disorganized handwriting.