Reading #8 A Lightweight Multistroke Recognizer for User Interface Prototypes

Comment:
Chris
Summary:
This paper is extention of previous 1$ recognizer. As same as 1$ recognizer, the purpose of this recognizer is not provide systematic and complex recognizer, instead it provides fast implementation, keep its simplicity. It mostly based on 1$ recognizer, but it makes great improvement that can recognizer multstroke symbols which is the limiatio of 1$ recognizer. Like 1$ recognizer, it consists of several steps. but the preprocessing steps is different from 1$ recognizer. For N$ recognzier, for each template symbol, it generates all the permutations of stroke combination, and the connect end points of consecutive strokes to make it unistroke. Aftet that, we can treat is unistroke just like in 1$ recognzier. Due to the computation explosion, the author gives some heuristic approach the prume some comparing templates. The author use start angle of stroke to eliminate unlikely matched candiate symbols. Instead of decreasing the accuracy of recognition, the author said this appraoch improve the recognizer accuracy. Another heuristic is to use number of strokes information, which futhur decrease the compuation cost. For the result part, the author said N$ recognizer gains about 96%, 97% accuracy rate.

Discussion:
Nice, simple, easy to understand approach to recognizer multistrokes. I pretty much like the idea of this paper. We can easily intergrate into our own system. However, as the author says, easy means there is limitation. For the large set of symbols, this approach is obvioulsy undergo high computation cost, which is also the limitation of template matching approach. There are might some improvements for N$ recognzier. 1. Intead of permuting all kinds of multistrokes, we can use certain more informative combination. 2. For the large symbol set. we can build binary tree to store these training data to improve the efficiency, clearly, we have to use some geometric features in that case to dinguish symbols.