Ensemble methods for handwritten text line recognition systems

This paper investigates the generation and use of classifier ensembles for offline handwritten text recognition. The ensembles are derived from the integration of a language model in the hidden Markov model based recognition system. The word sequences output by the ensemble members are aligned and combined according to the ROVER framework. The addressed environment is extreme because of the existence of a large number of word classes. Moreover, the recognisers do not produce single output classes but sequences of classes. Experiments conducted on the IAM database show that the ensemble methods are able to produce statistically significant improvements in the word level accuracy when compared to the base recogniser.