Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
Researchers have developed a highly accurate image classification system for historical documents, capable of distinguishing between text, tables, and graphics. Fine-tuned deep learning models, specifically RegNetY-16GF and ViT-large, achieved over 99% accuracy on a dataset of over 48,000 scanned pages. This system is designed to facilitate content-specific processing in large-scale digitization projects, with the models, dataset, and software made publicly available under open-source licenses. AI
IMPACT Enables efficient content-specific processing for large historical document archives, accelerating digitization efforts.