AUTOMATIC ESTIMATION OF THE READABILITY OF HANDWRITTEN TEXT
Andreas Schlapbach, Frank Wettstein, Horst Bunke · Bern Open Repository and Information System (University of Bern) · 2008
In this paper, the problem of estimating the readability of handwritten text is addressed. The estimation problem is posed as a two class classification problem where a text is classified as either readable or unreadable. A classifier is trained on this two class classification problem. In the train-ing phase, for each text a number of features are extracted. At the same time the recognition rate achieved on the text is determined. Based on the recognition rate, each feature vec-tor is labelled, i.e., assigned to one of the two classes. The labelled data is then used to train a classifier. The k-Nearest Neighbour (k-NN) and the Support Vector Machine (SVM) classifier are evaluated in this work. Both classifiers show promising results on a test set of 715 text lines from 20 writ-ers. 1.