Reading Assignment: PaleoSketch: Accurate Primitive Sketch Recognition and Beautification

Reference Information
Title: PaleoSketch: Accurate Primitive Sketch Recognition and Beautification
Authors: Brandon Paulson and Tracy Hammond
Citation: "PaleoSketch: Accurate Primitive Sketch Recognition and Beautification", Brandon Paulson and Tracy Hammond, Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 1-10, 2008.

Summary
This paper discussed a recognizer of low-level, primitive gestures that produces beautified versions of the gestures. The motivation behind the creation of this recognizer was to provide a means for integrating sketch recognition into user interfaces for freely-drawn sketches. The idea of the recognizer is to be able to recognize primitive gestures that then provide a foundation for creating more complex shapes by combining primitive shapes hierarchically. In order to improve upon other sketch recognition algorithms, two new features were added (NDDE and DCR) and a new ranking algorithm was used.

The recognizer works by taking a single stroke, calculating normalized distance between extremes (NDDE) and direction change ratio (DCR), then sending the data through a series of recognizers for each primitive that the system is designed to recognize (line, polyline, ellipse, circle, arc, curve, spiral, helix, and complex). The results of each recognizer are sorted into a hierarchy and ranked.

Experiments were conducted to collect drawn shapes from users, train the system on that data, and then test it against more data collected from users. The data was tested on both the recognizer described in this paper and other notable recognizers. It was shown that the recognizer has improved accuracy of recognition as compared to the other algorithms and that it also recognizes more primitives. Accuracy is most notably improved with regards to recognizing polylines and curves.

Thoughts
A motivation of this work that was discussed, providing an easier means of integrating sketch recognition into user interfaces, is very similar to that of the quill system that we read about in a previous reading assignment. Other topics that were mentioned in this paper that we have read about in previous assignments included the Sketchpad, Rubine, and Long work that were all cited as previous work that influenced the recognizer discussed within this paper. The previous reading assignment regarded a hybrid recognizer that was mentioned as a future goal within this paper.

I found the fact that a gesture is run through multiple recognizers, one for each primitive shape that the system is capable of recognizing, to be very interesting. Since the results of each are ranked, this would be useful for when a particular gesture is similar to multiple types of shapes, since each shape's likelihood of recognition would then be ranked. Also, the idea of building up complex shapes from a series of primitives seems like a very useful process for recognizing complex gestures.