Ricerc@Sapienza

Online separation between handwriting and freehand drawing is still an active research area
in the field of sketch-based interfaces. In the last years, most approaches in this area have
been focused on the use of statistical separation methods, which have achieved significant
results in terms of performance. More recently, Machine Learning (ML) techniques have
proven to be even more effective by treating the separation problem like a classification task.
Despite this, also in the use of these techniques several aspects can be still considered open
problems, including: 1) the trade-off between separation performance and training time; 2)
the separation of handwriting from different types of freehand drawings. To address the
just reported drawbacks, in this paper a novel separation algorithm based on a set of original
features and an Extreme Learning Machine (ELM) is proposed. Extensive experiments
on a wide range of sketched schemes (i.e., text and graphical symbols), more numerous
than those usually tested in any key work of the current literature, have highlighted the
effectiveness of the proposed approach. Finally, measurements on accuracy and speed of
computation, during both training and testing stages, have shown that the ELM can be considered,
in this research area, the better choice even if compared with other popular ML
techniques.