Experimental evaluation of a trainable scribble recognizer for calligraphic interfaces

Abstract

This paper describes a trainable recognizer for hand-drawn sketches using geometric features. We compare three different learning algorithms and select the best approach in terms of cost-performance ratio. The algorithms employ classic machine-learning techniques using a clustering approach. Experimental results show competing performance (95.1%) with the non-trainable recognizer (95.8%) previously developed, with obvious gains in flexibility and expandability. In addition, we study both their classification and learning performance with increasing number of examples per class. © Springer-Verlag Berlin Heidelberg 2002.