Segmenting handwritten text using supervised classification techniques

Recent work on extracting features of gaps in handwritten text allows a classification into inter-word and intra-word classes using suitable classification techniques. In this paper, we apply 5 different supervised classification algorithms from the machine learning field on both the original dataset and a dataset with the best features selected using mutual information. The classifiers are compared by employing McNemar's test. We find that SVMs and MLPs outperform the other classifiers and that preprocessing to select features works well.