Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation

In this paper, the segmentation of off-line cursive handwritten text lines into individual words is investigated. The problem is considered as a text line recognition task, adapted to the characteristics of segmentation. That is, at a certain position of a text line, it has to be decided whether the considered position belongs to a letter of a word, or to a space between two words. Thus the text line needs to be recognized as a sequence of non-space and space characters. For this purpose, three different recognizers based on hidden Markov models are designed, and results of writer-dependent as well as writer-independent experiments are reported in the paper.