To be able to profit from natural language processing (NLP) tools for analysing historical text, an important step is spelling normalisation. We first compare and second combine two different approaches: on the one hand VARD, a rule-based system which is based on dictionary lookup and rules with non-probabilistic but trainable weights; on the other hand a language-independent approach to spelling normalisation based on statistical machine translation (SMT) techniques. The rule-based system reaches the best accuracy, up to 94% precision at 74% recall, while the SMT system improves each tested period. We obtain the best system by combining both approaches. Re-training VARD on specific time-periods and domains is beneficial, and both systems benefit from a language sequence model using collocation strength.