Online Separation of Handwriting from Freehand Drawing Using Extreme Learning Machines

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.