Deep Learning Models Achieve 99% Accuracy Classifying Historical Document Page Images
Researchers developed and evaluated multiple deep learning architectures to automatically classify scanned page images from century-old Czech archaeological archives into 11 content categories (text, tables, graphics, etc.). The best-performing models—RegNetY-16GF and ViT-large—achieved over 99% accuracy on test data and successfully classified over 649,000 unlabeled archival pages. This work addresses a critical bottleneck in digital humanities projects where manual sorting of vast heterogeneous document collections is impractical.
A team of researchers developed an automated image classification system to sort scanned historical documents by visual content type, enabling efficient downstream processing like OCR and data extraction. The study evaluated multiple architectures—from traditional Random Forest classifiers using hand-crafted features (75% accuracy) to modern deep learning approaches including CNNs (EfficientNetV2, RegNetY), Vision Transformers (ViT), Document Image Transformers (DiT), and multimodal CLIP models. Training and evaluation used over 48,000 annotated page images from Czech archaeological archives, refined through four annotation stages with domain-expert review and an 11-category label scheme. The best image-only models achieved near-perfect test-set accuracy (RegNetY-16GF: 99.16%, ViT-large: 99.12%), with strong agreement when applied to 649,508 unlabeled pages. Notably, fine-tuned CLIP, despite competitive test accuracy, showed poor agreement with image-only models on unlabeled data (under 65%), making it unsuitable for deployment. The authors have released the models, annotated dataset, and software under open-source licenses.
What's missing
The study does not discuss computational costs or inference time comparisons between models, which would be relevant for practical deployment in resource-constrained digitization projects. Additionally, the generalizability of these models to document archives from other languages, time periods, or cultural contexts beyond Czech archaeological materials is not addressed.
What different sources said
- arXiv cs.AICenter
Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
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