Interactive Parts Model: An Application to Recognition of On-line Cursive Script

In this work, we introduce an Interactive Parts (IP) model as an alternative to Hidden Markov Models (HMMs). We tested both models on a database of online cursive script. We show that implementations ofHMMs and the IP model, in which all letters are assumed to have the same average width, give comparable results. However, in contrast to HMMs, the IP model can handle duration modeling without an increase in computational complexity. 1 Introduction Hidden Markov models [9] have been a dominant paradigm in speech and handwriting recognitionover the past several decades. The success of HMMs is primarily due to their ability to model the statistical and sequential nature of speech and handwriting data.