An Image-Based Trainable Symbol Recognizer for Sketch-Based
Interfaces  Levent Burak Kara and Thomas F. Stahovich  We
describe a trainable, hand-drawn symbol recognizer based on a
multi-layer recognition scheme. Symbols are internally
represented as binary templates. An ensemble of four template
classifiers ranks each definition according to similarity with
an unknown symbol. Scores from the individual classifiers are
then aggregated to determine the best definition for the
unknown. Ordinarily, template-matching is sensitive to rotation,
and existing solutions for rotation invariance are too expensive
for interactive use. We have developed an efficient technique
for achieving rotation invariance based on polar coordinates.
This techniques also filters out the bulk of unlikely
definitions, thereby simplifying the task of the multi-
classifier recognition step.