Temporal Sketch Recognition in Interspersed Drawings

Abstract

Sketch recognition has been recognized as an enabling technology for pen-based interfaces. Previous work in the field has shown that in certain domains the stroke orderings used when drawing objects contain temporal patterns that can aid recognition. So far, systems that use temporal information for recognition have assumed that objects are drawn one at a time. This paper shows how this assumption can be relaxed to permit temporal interspersing of strokes from different objects. We describe a statistical framework based on Dynamic Bayesian Networks that explicitly models the fact that objects can be drawn interspersed. We present recognition results for hand-drawn electronic circuit diagrams. The results show that handling interspersed drawing provides a significant increase in accuracy.