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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.

Layout Analysis for Camera-Based Whiteboard Notes