Comment :
Summary :
In this paper, Dean Rubine introduce his program GRANDMA and describe his single-stroke gesture recognizer which used linear classifier to recognize new single stroke.
In this paper, he describe 13 features he used to train recognizer, some of which are still widely used today. For machine learning algorithm, he used linear classifier, which is rather simple and easy to implement. Training process calculates the weight value for each feature for each class label. In the evaluation part, he shows that his recognizer has high accuracy. He also shows one very important fact that when training the recognizer, 15 examples per class is enough. Besides this, anther contribution of this paper was that he described rejection method for some ambiguous situation.
Discussion :
This paper plays very important role in sketch recognition field, and it tells the basic idea of building real time recognizer. Even though recognizer only handle single stroke, this work can be extended to build multi-stroke recognizer, which is very active field today. In fact, in real world, most of sketches are multi-strokes. We cannot put restriction that user only use single stroke to draw diagram or write characters. For extension part, Rubine briefly introduce two important things: Eager Recognition and Multi-finger recognition. For eager recognition, he said system can recognize while user was making it, this idea is very important for build real time system, but it must be able to give feedback and continually check and modify the recognition result by utilizing context information to maximize the overall confidence. I am pretty interest about multi-finger recognition, which is the most important feature of Apple product. I am also surprised about that this idea actually comes from decades ago. Perhaps in the near future, we don't need keyboard, mouse, what we only need are our fingers and mouth, which are enough to fully control computer and other electronic devices.
by Li 09/04/2010
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