Context modeling for text/non-text separation in free-form online handwritten documents

Free-form online handwritten documents contain a high diversity of content, organized without constraints imposed to the user. The lack of prior knowledge about content and layout makes the modeling of contextual information of crucial importance for interpretation of such documents. In this work, we present a comprehensive investigation of the sources of contextual information that can benefit the task of discerning textual from non-textual strokes in handwritten online documents. An in-depth analysis of interactions between strokes is conducted through the design of various pairwise clique systems that are combined within a Conditional Random Field formulation of the stroke labelling problem. Our results demonstrate the benefits of combining complementary sources of context for improving the text/non-text recognition performance.