A complement to variable duration hidden Markov model in handwritten word recognition

Because of large variation involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used both in speech and handwriting recognition. Basically, there are two strategies of using HMM: model discriminant HMM (MD-HMM) and path discriminant HMM (PD-HMM). Both of them have their advantages and disadvantages, and are discussed in this paper. Chen, Kundu and Sihari (see Proc. IEEE Int. Conference on Acoust., Speech, Signal Processing, (Minneapolis, Minnesota), p.V.105-108, April 1993) have developed a handwritten word recognition system using continuous density variable duration hidden Markov model (CDVDHMM), which belongs to the PD-HMM strategy. We describe a MD-HMM approach with the statistics derived from the CDVDHMM parameters. Detailed experiments are carried out; and the results using different approaches are compared