AI models achieve 99% accuracy classifying historical document images

Researchers have developed a highly accurate image classification system for historical documents, capable of distinguishing between text, tables, and graphics.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IMPACT Enables efficient content-specific processing for large historical document archives, accelerating digitization efforts.

RANK_REASON The cluster contains a research paper detailing a new AI model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• CLIP ViT-B/16
• Kateryna Lutsai Bc
• Random Forest Classifier
• RegNetY-16GF
• ViT-large

• paper
• product

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