Identifying Word Boundaries in Handwritten Text

Recent work on extracting features of gaps in handwritten text allows a classification of these gaps into inter-word and intra-word classes using suitable classification techniques. In the previous work, we apply different supervised classification algorithms from the machine learning field on both the original gap dataset and the gap dataset with the best features selected using mutual information. In this paper, we improve the classification result with the aid of a set of feature variables of strokes preceding and following each gap. The best classification result attained suggests that the technique we employ is particularly suitable for digital ink manipulation at the level of words.