Comments:
Amir
Summary:
Ink features are another name for... well ink features. Curvature, time, speed, intersection, and more are calculated and used to distinguish between different shapes and between shapes and text. In this paper, the authors look at different features to determine which ones actually aid in the shape/text division. A total of 46 ink features were tested over 1519 strokes drawn by 26 different participants. Each sample sketch included a mixture of text and shapes that the authors felt was representative of the overall use of computer-aided recognition.
In the end, 8 different features were found to really make a difference (as shown in this figure). Or do they? Upon testing, the authors found that using these ink features is beneficial, but that not all of them together provide the best results. Inter-stroke gaps, for instance, are much more helpful.
Discussion:
Making the distinction between shape and text is super easy for people, but super hard for computers. Constructing a feature set that can make this distinction with high accuracy would allow for crazy things to be done with computer-aided sketch recognition. It's frustrating when you have a domain that could benefit from the inclusion of handwriting and you find out that you suck at telling text apart from shapes. Someone needs to make this their thesis work.
Subset feature selection problem. It is so traditional, I bored with this idea.
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