Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Discerning Structure from Freeform Handwritten Notes
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Sketched Symbol Recognition using Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
SHARK2: a large vocabulary shorthand writing system for pen-based computers
Proceedings of the 17th annual ACM symposium on User interface software and technology
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sketch Interpretation Using Multiscale Models of Temporal Patterns
IEEE Computer Graphics and Applications
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
Properties of Real-World Digital Logic Diagrams
PLT '07 Proceedings of the First International Workshop on Pen-Based Learning Technologies
A pen-based tool for efficient labeling of 2D sketches
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
Newton's Pen: A pen-based tutoring system for statics
Computers and Graphics
Lineogrammer: creating diagrams by drawing
Proceedings of the 21st annual ACM symposium on User interface software and technology
Automatic evaluation of sketch recognizers
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Automatically transforming symbolic shape descriptions for use in sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A study of diagrammatic ink in lecture
Computers and Graphics
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
Recognition and beautification of multi-stroke symbols in digital ink
Computers and Graphics
An efficient graph-based symbol recognizer
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
A data collection tool for sketched diagrams
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
SOUSA: sketch-based online user study applet
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
Technical Section: Neural network-based symbol recognition using a few labeled samples
Computers and Graphics
Automated labeling of ink stroke data
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
RATA: codeless generation of gesture recognizers
BCS-HCI '12 Proceedings of the 26th Annual BCS Interaction Specialist Group Conference on People and Computers
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Generating, grouping, and labeling free-sketch data is a difficult and time-consuming task for both user study participants and researchers. To simplify this process for both parties, we would like to have users draw isolated shapes instead of complete sketches that must be hand-labeled and grouped, and then use this data to train our free-sketch symbol recognizer. However, it is an open question whether shapes drawn in isolation accurately reflect the way users draw shapes in a complete diagram. To answer this question, we present a systematic exploration of the effect of task on recognition accuracy using three different recognizers. Our study examines how task affects accuracy in the context of user-independent, user semi-dependent and user-dependent training data. We find that as the amount of user-specific training data increases, the effect of task on recognition accuracy also increases. We also show that the best overall recognition results are obtained by using user semi-dependent, task-specific training data. These results hold across three different domains: circuit diagrams, entity relationship diagrams and process diagrams. Finally, we introduce a variant of a popular and simple gesture recognition algorithm that recognizes freely drawn shapes as well as a highly accurate but more complex recognizer designed explicitly for free-sketch recognition.