  We present a novel approach to lexical error recovery on textual input. An
advanced robust tokenizer has been implemented that can not only correct
spelling mistakes, but also recover from segmentation errors. Apart from the
orthographic considerations taken, the tokenizer also makes use of linguistic
expectations extracted from a training corpus. The idea is to arrange Hidden
Markov Models (HMM) in multiple layers where the HMMs in each layer are
responsible for different aspects of the processing of the input. We report on
experimental evaluations with alternative probabilistic language models to
guide the lexical error recovery process.
