An introduction to digital image processing
An introduction to digital image processing
A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Towards Automatic Video-based Whiteboard Reading
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Markov Models for Pattern Recognition: From Theory to Applications
Markov Models for Pattern Recognition: From Theory to Applications
Markov models for offline handwriting recognition: a survey
International Journal on Document Analysis and Recognition
a.SCAtch - A Sketch-Based Retrieval for Architectural Floor Plans
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
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Recognizing mind maps written on a whiteboard is a challenging task due to the unconstrained handwritten text and the different graphical elements -- i.e. lines, circles and arrows -- available in a mind map. In this paper we propose a prototype system to recognize and visualize such mind maps written on whiteboards. After the image acquisition by a camera, a binarization process is performed, and the different connected components are extracted. Without presuming any prior knowledge about the document, its style, layout, etc., the analysis starts with connected components, labeling them as text, lines, circles or arrows based on a neural network classifier trained on some statistical features extracted from the components. Once the text patches are identified, word detection is performed, modeling the text patches by their gravity centers and grouping them into possible words by density based clustering. Finally, the grouped connected components are recognized by a Hidden Markov Model based recognizer. The paper also presents a software tool integrating all these processing stages, allowing a digital transcription of the mind map and the interaction between the user, the mind map, and the whiteboard.