Hinkley et al. introduce InkSeine, a system for bring non-sketch items into sketches. While a user is taking some notes, she can use the in situ search to bring in external items, all with the pen in a natural way.
InkSeine seems like a good way to pair notetaking with research, and it's nice not to have to switch back and forth from pen to keyboard to do both tasks.
Plushie is a system for turning a Teddy (Reading #21) model into designs that can be printed out to make a real-life plush toy that looks just like the model. The Teddy interface is augmented with some operations that will help the user more easily sew the pattern together.
This is also a neat system, though my interest in 3D sketching and plush-toy creation is pretty limited.
Igarashi introduces Teddy, a system for turning 2D sketches into 3D models of plush toys. Teddy also introduces some interaction techniques for editing the models.
Teddy is a fun system to play with, although it's kind of frustrating if you're as bad of an artist as I am.
Mathpad2 is a cool interactive system for students to be able to visualize and work on math problems. The recognition is largely handled by Microsoft, but LaViola and Zeleznik introduce a cool trick for grouping characters in a sketch and also some neat interaction gestures (like the tapping).
After working so much on Mechanix, I feel like I can really relate to Mathpad2, a system with similar goals.
Qi et al. present a grouping approach based on Conditional Random Fields. Conditional random fields are cool because they can take into account both local features of a stroke and also the stroke's interactions with the other strokes nearby.
There's a great payoff for learning it all, but this math is just so complicated and I just can't stay focused with so many equations all over the place.
Shilman and Viola present an application of AdaBoost to simultaneously approach the grouping and recognition problems. The idea is simple: a group of strokes belong together if it can be recognized as something.
It's nice to see an approach that doesn't just assume the grouping problem will be solved by someone else. There are so many symbol recognition papers that make such an assumption, but not very many papers that actually provide any contribution toward solving that assumption.
This paper presents a couple of HMM-based approaches for distinguishing text from graphics. The recognizer presented is probably similar to the one implemented by Microsoft, as described in the Patel et al. paper before.
This paper does a good job of discussing some of the challenges of text vs shape classification, especially the heavy bias toward text in most of the data that the authors collected.
Left click anywhere to start drawing. Right click to attempt recognition and to clear the screen. Recognition is powered by the $N recognizer. Contact me if you want your blog listed; for now please pick a symbol from that web page.
