Whiteboards support face to face meetings by facilitating the sharing of ideas, focusing attention, and summarizing.
However, at the end of the meeting participants desire some record of the information from the whiteboard. While there
are whiteboards with built-in printers, they are expensive and relatively uncommon. We consider the capture of the
information on a whiteboard with a mobile phone, improving the image quality with a cloud service, and sharing the
results. This paper describes the algorithm for improving whiteboard image quality, the user experience for both a web
widget and a smartphone application, and the necessary adaptations for providing this as a web service. The web widget,
and mobile apps for both iPhone and Android are currently freely available, and have been used by more than 50,000
The variety of displays used to browse and view images has created a need to adapt an image representation to
constraints given by the viewing environment. In this paper various methods of adaptation to a small display size are
introduced with focus on adaptation of document images.
Compared to photographic images, document images pose an even greater challenge to represent on small size displays.
If a typical down-sampling of image data is performed, we not only loose some high-resolution data, but also semantic
information, such as readability, recognizability, and distinguishability of features.
We explore various ways of controlling document information such as readable text or distinguishable layout features in
different visualizations applying specific content-dependent scaling methods. Readability is preserved in "SmartNails"
via automatic content-dependent cropping, scaling and pasting. Content-dependent iconification is proposed to provide
distinguishability between layout features of document images. In the case of multi-page document content a rendering
in form of a video clip is proposed that performs content-dependent navigation through the image data given display size
and time constraints.
Proc. SPIE. 6073, Multimedia Content Analysis, Management, and Retrieval 2006
KEYWORDS: Visualization, Databases, Image segmentation, Digital cameras, Feature extraction, Image classification, Optical character recognition, Computer graphics, Information visualization, Classification systems
Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.