  Natural-language processing of historical documents is complicated by the
abundance of variant spellings and lack of annotated data. A common approach is
to normalize the spelling of historical words to modern forms. We explore the
suitability of a deep neural network architecture for this task, particularly a
deep bi-LSTM network applied on a character level. Our model compares well to
previously established normalization algorithms when evaluated on a diverse set
of texts from Early New High German. We show that multi-task learning with
additional normalization data can improve our model's performance further.
