Word segmentation of handwritten text using supervised classification techniques Export

@article{citeulike:820303, abstract = {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 this paper, we first analyse the features of the gaps using mutual information. We then investigate the underlying data distribution by using visualisation methods. These suggest that a complicated structure exists, which makes them difficult to be separated into two distinct classes. We apply five 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. Moreover, we improve the classification result with the aid of a set of feature variables of strokes preceding and following each gap. 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. The best classification result attained suggests that the technique we employ is particularly suitable for digital ink manipulation at the level of words.}, author = {Sun, Yi and Butler, Timothy S. and Shafarenko, Alex and Adams, Rod and Loomes, Martin and Davey, Neil}, citeulike-article-id = {820303}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.asoc.2004.10.009}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6W86-4GCXJGH-1/2/de47b96ba1a6d30ae2077ecff14fb079}, doi = {10.1016/j.asoc.2004.10.009}, journal = {Applied Soft Computing}, keywords = {classification, handwritten, segmentation, word}, posted-at = {2006-08-29 02:32:09}, priority = {5}, title = {Word segmentation of handwritten text using supervised classification techniques}, url = {http://dx.doi.org/10.1016/j.asoc.2004.10.009}, volume = {In Press, Corrected Proof} }

TY - JOUR ID - citeulike:820303 L3 - citeulike-article-id:820303 N2 - 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 this paper, we first analyse the features of the gaps using mutual information. We then investigate the underlying data distribution by using visualisation methods. These suggest that a complicated structure exists, which makes them difficult to be separated into two distinct classes. We apply five 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. Moreover, we improve the classification result with the aid of a set of feature variables of strokes preceding and following each gap. 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. The best classification result attained suggests that the technique we employ is particularly suitable for digital ink manipulation at the level of words. JF - Applied Soft Computing TI - Word segmentation of handwritten text using supervised classification techniques VL - In Press, Corrected Proof KW - classification KW - handwritten KW - segmentation KW - word AU - Sun, Yi AU - Butler, Timothy AU - Shafarenko, Alex AU - Adams, Rod AU - Loomes, Martin AU - Davey, Neil UR - http://dx.doi.org/10.1016/j.asoc.2004.10.009 ER -