Historical document dating using unsupervised attribute learning

J.W.J. Burgers, Sheng He, Petros Samara, L.R.B. Schomaker

Research output: Contribution to journal/periodicalArticleScientificpeer-review


The date of historical documents is an important metadata for scholars using them, as they need to know the historical context of the documents. This paper presents a novel attribute representation for medieval documents to automatically estimate the date information, which are the years they had been written. Non-semantic attributes are discovered in the low-level feature space using an unsupervised attribute learning method. A negative data set is involved in the attribute learning to make sure that our system rejects the documents which are not from the Middle Ages nor from the same archives. Experimental results on the basis of the Medieval Paleographic Scale (MPS) data set demonstrate that the proposed method achieves the state-of-the-art result
Original languageEnglish
Pages (from-to)36
Number of pages41
JournalProceedings of the 12th IAPR workshop on document analysis systems
Publication statusPublished - 2016


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