We present a multilingual evaluation of approaches for spelling normalisation of historical text based on data from five languages: English, German, Hungarian, Icelandic, and Swedish. Three different normalisation methods are evaluated: a simplistic filtering model, a Levenshtein-based approach, and a character-based statistical machine translation approach. The evaluation shows that the machine translation approach often gives the best results, but also that all approaches improve over the baseline and that no single method works best for all languages.
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