Including Language Model Information in the Combination of Handwritten Text Line Recognisers

This paper proposes a novel language model based combination method for ensembles of offline handwritten text line recognisers. The individual recognisers are based on hidden Markov models and the ensembles are generated with the bagging method. The proposed combination method extends the ROVER framework by rescoring the word transition networks with a language model. Experiments conducted on a large database of offline handwritten text lines show that the proposed approach can improve the recognition accuracy over a reference system as well as over the original ROVER combination method.