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Camera-Based Whiteboard Reading for Understanding Mind Maps

Lookup NU author(s): Dr Thomas Ploetz



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Mind maps, i.e., the spatial organization of ideas and concepts around a central topic and the visualiza- tion of their relations, represent a very powerful and thus popular means to support creative thinking and problem solving processes. Typically created on traditional whiteboards, they represent an important technique for collaborative brainstorming sessions. We describe a camera-based system to analyze hand-drawn mind maps written on a whiteboard. The goal of the presented system is to produce digital representations of such mind maps, which would enable digital asset management, i.e., storage and retrieval of manually created documents. Our system is based on image acquisition by means of a camera followed by the segmentation of the particular whiteboard image focusing on the extraction of written context, i.e., the ideas captured by the mind map. The spatial arrangement of these ideas is recovered using layout analysis based on unsupervised clustering, which results in graph representations of mind maps. Finally, handwriting recognition derives textual transcripts of the ideas captured by the mind map. We demonstrate the capabilities of our mind map reading system by means of an experimental evaluation, where we analyze images of mind maps that have been drawn on whiteboards, without any further constraints other than the underlying topic. In addition to the promising recognition results, we also discuss training strategies, which effectively allow for system bootstrapping using out-of-domain sample data. The latter is important when addressing creative thinking processes where domain-related training data are difficult to obtain as they focus on novelty by definition.

Publication metadata

Author(s): Vajda S, Ploetz T, Fink G

Publication type: Article

Publication status: Published

Journal: International Journal of Pattern Recognition and Artificial Intelligence

Year: 2015

Volume: 29

Issue: 3

Print publication date: 01/05/2015

Online publication date: 30/03/2015

Acceptance date: 02/01/2015

Date deposited: 22/05/2015

ISSN (print): 0218-0014

ISSN (electronic): 1793-6381

Publisher: World Scientific Publishing Co. Pte. Ltd.


DOI: 10.1142/S0218001415530031


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