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:

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.