Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
ACM Computing Surveys (CSUR)
Supporting electronic ink databases
Information Systems
Visual similarity of pen gestures
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Dual touch: a two-handed interface for pen-based PDAs
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Guided gesture support in the paper PDA
Proceedings of the 14th annual ACM symposium on User interface software and technology
GIA: design of a gesture-based interaction photo album
Personal and Ubiquitous Computing
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
Kernel class-wise locality preserving projection
Information Sciences: an International Journal
Facial feature localization based on an improved active shape model
Information Sciences: an International Journal
A lightweight multistroke recognizer for user interface prototypes
Proceedings of Graphics Interface 2010
Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot
Information Sciences: an International Journal
Facial expression feature selection based on rough set
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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Electronic ink is a form of multimedia data that has recently emerged from the development of pen-based computers which incorporate a stylus pen as the major input device. Gestures have proven to be a useful feature for pen-based user interface. In this paper, we studied some techniques in order to develop a gesture-allowed electronic ink editor for PDAs with hardware constraints. We designed eleven gestures, presented a new feature-based recognition algorithm to identify gestures, and proposed a method for segmenting ink data which mainly consists of Korean scripts into logical units. We implemented GesEdit, the gesture-allowed ink editor on PDAs using the proposed methods. A variety of experiments involving twenty users showed that a gesture recognition rate reaching 99.6% and a correct segmentation rate surpassing 99.8%.