Dynamic observations and dynamic state termination for off-line handwritten word recognition using HMM

HMM has been successfully used to model 1D data, e.g. voice signals. Their use to model 2D patterns was not as successful due to a major difficulty, in describing the 2D data using 1D observation sequences. In this paper, we discuss the importance of this issue and present an improved method to extract 1D observations from the dynamics of off-line handwritten words. The method is based on pen trajectory estimation techniques. The paper also includes description of our HMM classifier which allows dynamic termination states to achieve enhanced discriminative power. Experimental results show the applicability and usefulness of the proposed method. As a result of using the termination probability in HMM modeling, the top 1^st recognition rate increased by 10%.