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This paper presents an extensive investigation of the use of trigraphs for online cursive handwriting recognition based on hidden Markov models (HMMs). Trigraphs are context dependent HMMs representing a single written character in its left and right context, similar to triphones in speech recognition. Looking at the great success of triphones in continuous speech recognition, it was always a challenging and open question, if the introduction of trigraphs could lead to substantially improved handwriting recognition systems. The results of this investigation are indeed extremely encouraging: the introduction of suitable trigraphs led to a 50% relative error reduction for a writer dependent 1000 word handwriting recognition system, and to a 35% relative error reduction for the same system with an extended 30000 word vocabulary for cursive handwriting recognition.
Kosmala et al. (Fri,) studied this question.