Reading #8. A Lightweight Multistroke Recognizer for User Interface Prototypes

Comments on Others:

Youyou Wang 

Summary
           The paper describes $N recognizer, a lightweight, concise multi-stroke recognizer, which is a significant extension to the $1 uni-stroke recognizer.  The $N said to be capable of user defined complex gesture identification, customization, and operate at speeds supporting fluid interaction (not sure what it means though???). $N is an extension of $1 to overcome limitations of that version, such as uni-stroke nature, failure to recognize 1D gestures, and rotation invariance. More specifically, the $N recognizer is implemented to use with rapid prototyping as way of quickly incorporating it to user interfaces with small loc size.
           The intuitive idea behind the $N development is to support rapid prototyping applications by eliminating the need of permutations of multistroke gesture and enable to enter only a single version of the gesture and use it for recognition. This is done by creating unistroke permutations of the multistroke at the define time and then using those for comparison at the run time. The $N uses automatic differentiating using a threshold to identify 1D and 2D gestures so that 1D’s can be preserved its aspect ratio.
           The paper also describes $N limitations, such as provisions for scale and position, and not using gesture features. 

Discussion
            First the use of name $N kind a amusing, may be they took the idea of $1 literally and replace 1 by N to represent the idea of multi-stroke possibility in their algorithm recognition compared to $1 recognizers uni-stroke nature. But I guess $1 recognizer authors used that name to represent both how simple it is, less expensive (shorter implementation time and small size) and uni-stroked. But when it comes to $N, the idea comes to readers mind is this thing N times expensive to implement (that much complex) than $1 recognizer (don’t laugh, that’s how I feel).
           One of the best goal of the $N as I consider is the ability to employ recognition with minimal input support (just a single multistroke entry) compared to other available recognizers including $1.  The use of just the Euclidian distance for comparison between the candidate stroke to permutations unistroke is questionable, and in my opinion not the correct technique to do so. May be a RMSE value between the candidate and the unistroke permutation may be a good idea to verify the accuracy and may be to increase the performance.
           The $N requires separate step to do the recognition of 1D from 2D, and in my opinion this can be avoided by using size invariant algorithmic design, may be a comparison based on the basic segment structure (example: a square consists of similar lengthen 2 horizontal and 2 vertical lines, and a triangle consists of 2 slanted and 1 horizontal lines).

Find the paper here.