Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
A Fast Statistical Mixture Algorithm for On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive sketching for the early stages of user interface design
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Ambiguous intentions: a paper-like interface for creative design
Proceedings of the 9th annual ACM symposium on User interface software and technology
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Structure in On-line Documents
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
SketchIT: a Sketch Interpretation Tool for Conceptual Mechanical Design
SketchIT: a Sketch Interpretation Tool for Conceptual Mechanical Design
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
Distinguishing Text from Graphics in On-Line Handwritten Ink
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Ink features for diagram recognition
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
GLADDER: combining gesture and geometric sketch recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
LADDER: a language to describe drawing, display, and editing in sketch recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
SOUSA: sketch-based online user study applet
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
The power of automatic feature selection: Rubine on steroids
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Using scribble gestures to enhance editing behaviors of sketch recognition systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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Most sketch recognition systems are accurate in recognizing either text or shape (graphic) ink strokes, but not both. Distinguishing between shape and text strokes is, therefore, a critical task in recognizing hand-drawn digital ink diagrams that contain text labels and annotations. We have found the 'entropy rate' to be an accurate criterion of classification. We found that the entropy rate is significantly higher for text strokes compared to shape strokes and can serve as a distinguishing factor between the two. Using a single feature -- zero-order entropy rate -- our system produced a correct classification rate of 92.06% on test data belonging to diagrammatic domain for which the threshold was trained on. It also performed favorably on an unseen domain for which no training examples were supplied.