Clustering Historical Documents Based on the Reconstruction Error of Autoencoders
Mathias Seuret|Andreas Fischer|Angelika Garz|Marcus Liwicki|Rolf Ingold
The term documents encompasses an enormous variety of document types considering different scripts, languages, writing supports, and degradation degrees. For automatic processing with machine learning pattern recognition methods, it would be ideal to share labeled samples trained statistical models across similar documents, avoiding a retraining from scratch for every historical anew. In this paper, we propose using the reconstruction error autoencoders compare manuscripts goal clustering them according their visual appearance. A low suggests similarity between new manuscript known manuscript, which autoencoder was in unsupervised fashion. Preliminary experiments conducted on 10 written ink parchment demonstrate ability group styles. discriminating Carolingian cursive script, particular, near-perfect results are reported.