Ink Features for Diagram Recognition

Summary:

Patel et al. use ink features to divide a sketch into text and shapes via a dividing tree. Forty-six features were defined by the authors for use in the divider, all of which were defined in an appendix.

Sketch data was collected from 26 people, each person drawing 9 sketches. Each stroke in the sketch was then labeled as being part of text or a shape, and analysis was performed on these sketches to determine the relevant features distinguishing between the two components. The authors used a statistical partitioning technique available in the R statisical package to divide the data into two components in the feature space and determine the most relevant features. A classification tree was then built with the most relevant feature as the root of the tree, and each node defines a threshold that separates the space into text and shape components.

The authors obtained great results with the simple classification tree technique. The new classifier greatly reduced the number of misclassified strokes as a whole from both Microsoft's classifier and InkKit's. Although in the test results the new classifier misclassifies text a bit more than either baseline system, text misclassification is much lower in the new system.

Discussion:

The actual accuracy numbers are not impressive, since the system still misclassifies too many strokes to be considered "accurate", but the system's improvement is very impressive and pushes the research on text/shape classification into a new direction. The author's analysis of the features (and inclusion of the features in an appendix!) was appreciated, and they discussed which features the Rpart partitioning system found most helpful. Since this model should run very quickly after training, it could easily be combined with other models to improve text/shape recognition even more.