Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams

Most sketch recognition systems are accurate in recognizing either text or shape (graphic) ink strokes, but not both. Distinguishing between shape and text strokes is, therefore, a critical task in recognizing hand-drawn digital ink diagrams that contain text labels and annotations. We have found the 'en-tropy rate' to be an accurate criterion of classification. We found that the entropy rate is significantly higher for text strokes compared to shape strokes and can serve as a distinguishing factor between the two. Using a single feature – zero-order entropy rate – our system produced a correct classification rate of 92.06% on test data belonging to diagram-matic domain for which the threshold was trained on. It also performed favorably on an unseen domain for which no training examples were supplied .