Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Visual similarity of pen gestures
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
DENIM: finding a tighter fit between tools and practice for Web site design
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
A domain-independent system for sketch recognition
Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia
A Strategy for On-line Interpretation of Sketched Engineering Drawings
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Pen Pressure Features for Writer-Independent On-Line Handwriting Recognition Based on Substroke HMM
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Sketch based interfaces: early processing for sketch understanding
Proceedings of the 2001 workshop on Perceptive user interfaces
Distinguishing Text from Graphics in On-Line Handwritten Ink
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
PaleoSketch: accurate primitive sketch recognition and beautification
Proceedings of the 13th international conference on Intelligent user interfaces
Ink features for diagram recognition
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
A toolkit approach to sketched diagram recognition
BCS-HCI '07 Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI...but not as we know it - Volume 1
Automatic evaluation of sketch recognizers
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Computational Support for Sketching in Design: A Review
Foundations and Trends in Human-Computer Interaction
Iconic and multi-stroke gesture recognition
Pattern Recognition
Using entropy to distinguish shape versus text in hand-drawn diagrams
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Rata.SSR: data mining for pertinent stroke recognizers
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
A comparative evaluation of finger and pen stroke gestures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Small gestures go a long way: how many bits per gesture do recognizers actually need?
Proceedings of the Designing Interactive Systems Conference
Mobile vision-based sketch recognition with SPARK
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
Relative accuracy measures for stroke gestures
Proceedings of the 15th ACM on International conference on multimodal interaction
Analyzing touchless hand gestures performance
Proceedings of the 2013 Chilean Conference on Human - Computer Interaction
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Digital ink features drive recognition engines. Intuitively, we understand that particular features are of more value for some problems than others. Likewise, inclusion of poor features may be detrimental to recognition success. Many different ink features have been proposed for ink recognition, and most work well for the context that they are employed. However given a new problem it is not clear which of the already defined features will be most useful. We have assembled and categorized a comprehensive feature library and use this with attribute selection algorithms to choose the best features for a specified problem. To verify the effectiveness of this approach the selected features are used to train a Rubine's recognizer. We show that a set of complementary features is most effective: poor features adversely affect recognition as do two or more aliases of good features. We have composed a variant of a Rubine recognizer for 3 different datasets and compared these with the Rubine's original features, a variant on this InkRubine and $1. The results show that feature selection can significantly improve recognition rates with this simple algorithm thus verifying our hypothesis that the right combination of features for a problem is one key to recognition success.