Page Embeddings: Extracting and Classifying Historical Documents with Generic Vector Representations

We propose a neural network architecture designed to generate region and page embeddings for boundary detection and classification of documents within a large and heterogeneous historical archive. Our approach is versatile and can be applied to other tasks and datasets. This method enhances the accessibility of historical archives and promotes a more inclusive utilization of historical materials.