Improving digital ink interpretation through expected type prediction and dynamic dispatch

Interpretation accuracy of current handwriting applications can be improved by providing contextual information about an ink samplepsilas expected type. We have developed a novel approach that uses a classic machine learning technique to predict this expected type from an ink sample. With this approach, we can create a ldquodynamic dispatch interpreterrdquo by biasing interpretation differently according to the predicted expected types of the ink samples. When evaluated in the domain of introductory computer science, our interpreter achieves high interpretation accuracy (87%), an improvement from Microsoftpsilas default interpreter (62%), and comparable with other previous interpreters (87-89%), which, unlike ours, require additional user-specified expected type information for each ink sample.