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.