A new combination scheme for HMM-based classifiers and its application to handwriting recognition

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. The combination of multiple classifiers has been proven to be able to increase the recognition rate when compared to single classifiers. In this paper a new combination method for HMM based handwritten word recognizers is introduced. In contrast with many other multiple classifier combination schemes, where the combination takes place at the decision level, the proposed method combines various HMMs at a more elementary level. The usefulness of the new method is experimentally demonstrated in the context of a handwritten word recognition task.