With the increasing availability of digitized resources of historical documents, interest in
effective natural language processing (NLP) for these documents is on the rise. However,
the abundance of variant spellings makes them challenging to work with both for human
users and for NLP tools. Normalization to contemporary spelling is often proposed as a
solution. This work investigates the suitability of a neural encoder–decoder architecture for
automatic normalization of historical language data. The neural network is extensively tuned …