Deep Learning Models Achieve 99% Accuracy Classifying Historical Document Page Images
Researchers developed and evaluated an automated image classification system capable of sorting over 48,000 annotated historical scanned pages from century-old Czech archaeological archives into 11 content categories with up to 99.16% accuracy. The system compared multiple deep learning architectures—including CNNs, Vision Transformers, and multimodal CLIP models—against a hand-crafted feature baseline that achieved roughly 75% accuracy. The work addresses a critical bottleneck in large-scale humanities digitization projects, where manual sorting of heterogeneous archival material is impractical.
A team of researchers fine-tuned and benchmarked several deep learning architectures on a dataset of more than 48,000 annotated page images drawn from century-old Czech archaeological archives, aiming to automate the classification of scanned documents by visual content type—text, tables, and graphics. The annotation process was refined across four successive stages with domain-expert review, and an 11-category label scheme was developed collaboratively with those experts. A Random Forest Classifier using hand-crafted image features served as a baseline, reaching approximately 75% accuracy, while fine-tuned convolutional and transformer models substantially outperformed it: RegNetY-16GF achieved 99.16% and ViT-large 99.12% Top-1 accuracy on the held-out test set. Notably, CLIP ViT-B/16 matched competitive test-set accuracy at 99.14% but showed less than 65% agreement with image-only models when applied to 649,508 unlabeled archival pages, raising concerns about its reliability for real-world deployment. Image-only models demonstrated over 90% inter-model agreement on the unlabeled data, making them the preferred choice for production use. All final models, the annotated dataset, and associated software have been released under open-source licenses, facilitating adoption by other digitization projects in the humanities.
What's missing
The study does not report computational costs or inference time for the evaluated architectures, which are relevant practical considerations for institutions running large-scale digitization pipelines. It is also unclear how well the models would generalize to archival collections from other languages, regions, or time periods beyond the Czech archaeological corpus used here.
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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