Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
arXiv:2606.07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale. This work addresses the need for an automated system to classify scanned page images based on visual content type - text, tables, and graphics - enabling content-specific downstream processing such as Optical Character Recognition (OCR) or structured data extraction. Methods: An image classification system was developed and evaluated on a dataset of over 48,000 annotated historical page image
The proliferation of digitized historical archives, combined with advancements in deep learning and image classification, now makes automated document processing at scale feasible and necessary.
This development enables efficient unlocking of vast amounts of historical data, transforming research in humanities and creating new opportunities for content extraction and analysis.
Manual sorting of large historical document archives becomes obsolete, replaced by automated, content-specific classification that significantly accelerates downstream processing like OCR.
- · Humanities researchers
- · Digital archivists
- · AI/ML developers
- · OCR software providers
- · Manual data entry services
- · Traditional archival labor
Vast quantities of previously unsearchable historical document content become accessible and analyzable.
New historical insights and research avenues emerge from the ability to process and cross-reference documents at an unprecedented scale.
The development of more sophisticated AI models specifically tailored for historical document understanding, potentially leading to new forms of digital humanities scholarship and tools.
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Read at arXiv cs.AI