Cyclic Viterbi Score for Linear Hidden Markov Models.

Hidden Markov Models (HMM) have been successfully applied to describe sequences of observable events. In some problems, objects are more appropriately described as cyclic sequences, i.e., sequences with no begin/end point. Conventional HMMs with Viterbi score cannot deal adequately with cyclic sequences. We propose a cyclic Viterbi score that can be efficiently computed for Linear HMMs. Linear HMMs model sequences that can be partitioned into contiguous segments where each state is responsible for emitting all symbols in one of the segments. Experiments show that our proposal outperforms other approaches in an isolated characters handwritten-text recognition task. [ABSTRACT FROM AUTHOR]