In this paper we propose an approach to tagging and parsing of historical text, using characterbased

SMT methods for translating the historical spelling to a modern spelling before applying

the NLP tools. This way, existing modern taggers and parsers may be used to analyse historical

text instead of training new tools specialised in historical language, which might be hard

considering the lack of linguistically annotated historical corpora. We show that our approach

to spelling normalisation is successful even with small amounts of training data, and that

it is generalisable to several languages. For the two languages presented in this paper, the

proportion of tokens with a spelling identical to the modern gold standard spelling increases

from 64.8% to 83.9%, and from 64.6% to 92.3% respectively, which has a positive impact on

subsequent tagging and parsing using modern tools.