---
title: "Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams"
authors:
  - "Akshay Bhat"
  - "Tracy Hammond"
doi: ""
url: "https://ijcai.org/proceedings/2009/234"
pdf: "https://ijcai.org/proceedings/2009/0234.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "1395-1399"
track: ""
keywords:
---

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

Akshay Bhat, Tracy Hammond

*Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence*, pages 1395-1399.

## Abstract

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 which commonly contain many text labels and annotations. We have found the ‘entropy 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 entropy values, our system produced a correct classification rate of 92.06% on test data belonging to diagrammatic domain for which the threshold was trained on. It also performed favorably on data for which no training examples at all were supplied.Akshay Bhat, Tracy Anne Hammond