Using Machine Learning to Improve the Whiteboard Experience


Virtual meetings and conferences are getting more common and more mainstream in the workplace. This shift in use of technology means that other work practices has to adapt to work with virtual meetings. One such practice is the use of writing on whiteboards. Just like some people prefer to read from a book instead of a monitor, a practice like writing on a whiteboard might never be replaced by writing on a tablet. This creates a set of problems of how can the whiteboard be integrated to work with the virtual world. There’s many ideas and potential solutions for this like using text recognition, but most of these solutions require the whiteboard to be detected in the first place. To solve this issue, a whiteboard detection model is proposed which is composed of a convolutional neural net to classify whiteboards in real-time videos through semantic image segmentation and computer vision to process the outline of the classified whiteboards into a set of points which can be used for further analysis and processing.