Layout Analysis for Camera-Based Whiteboard Notes

A domain where, even in the era of electronic document processing, hand- writing is still widely used is note-taking on a whiteboard. such documents are either captured by a pen-tracking device or – which is much more challenging – by a cam- era. in both cases the layout analysis of realistic whiteboard notes is an open research problem. in this paper we propose a camera-based three-stage approach for the automatic lay- out analysis of whiteboard documents. assuming a reasonable foreground-background separation of the handwriting it starts with a locally adaptive binarization followed by connected component extraction. the latter are then automatically classified as repre- senting either simple graphical elements of a mindmap or elementary text patches. in the final stage the text patches are subject to a clustering procedure in order to gener- ate hypotheses for those image regions where textual annotations of the mindmap can be found. in order to demonstrate the effectiveness of the proposed approach we report results of a writer independent experimental evaluation on a data set of mindmap images created by several different writers without any constraints on writing or drawing style.