Reading #13. Ink Features for Diagram Recognition (Plimmer)

Summary

In this paper, the author introduces a study done to determine the statistically best ink features for recognition of shapes versus text. About 1500 strokes of data were collected, labeled and the selected features were computed for each. This information was then fed into the R Statistical Package which provided a binary decision tree containing the most significant features.

Discussion

This paper provided valuable insight into which features are most useful in distinguishing shape stroke from text strokes. However, a binary decision tree can lead to misclassification when only a single a feature is not drawn in the same manner as the training data. Once the statistically important features were determined, the use of a linear classifier or some other method may have been preferable.