Experimental Evaluation of a Trainable Scribble Recognizer for Calligraphic Interfaces

  • César F. Pimentel
  • Manuel J. da Fonseca
  • Joaquim A. Jorge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2390)


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.


Recognition Rate Near Neighbor Solid Shape Inductive Decision Gesture Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • César F. Pimentel
    • 1
  • Manuel J. da Fonseca
    • 1
  • Joaquim A. Jorge
    • 1
  1. 1.Departamento de Engenharia InformáticaIST/UTLLisboaPortugal

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