Mode detection in online pen drawing and handwriting recognition
Proceedings of the Eight international conference on document analysis and recognition, page 31-35. (2005)Abstract
On-line pen input benefits greatly from mode detection when the user is in a
free writing situation, where he is allowed to write, to draw, and to generate
gestures. Mode detection is performed before recognition to restrict the
classes that a classifier has to consider, thereby increasing the performance
of the overall recognition. In this paper we present a hybrid system which is
able to achieve a mode detection performance of 95.6\% on seven classes;
handwriting, lines, arrows, ellipses, rectangles, triangles, and diamonds. The
system consists of three kNN classifiers which use global and structural
features of the pen trajectory and a fitting algorithm for verifying the
different geometrical objects. Results are presented on a significant amount of
data, acquired in different contexts like scribble matching and design
applications.
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