Mode detection in on-line pen drawing and handwriting recognition

Authors:
Don Willems;Stephane Rossignol;Louis Vuurpijl
Affiliations:
Radboud University Nijmege, Netherlands;Radboud University Nijmege, Netherlands;Radboud University Nijmege, Netherlands
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

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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.