Mode detection in on-line pen drawing and handwriting recognition

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.

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Don Willems, Stéphane Rossignol, Louis Vuurpijl
• Keywords: Document Analysis, ICDAR 2005, Mode Detection, Mode Detection Performance, On-line Pen Input