Recognizing multistroke geometric shapes: an experimental evaluation
UIST '93 Proceedings of the 6th annual ACM symposium on User interface software and technology
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Sketch interpretation using multiscale stochastic models of temporal patterns
Sketch interpretation using multiscale stochastic models of temporal patterns
Iconic and multi-stroke gesture recognition
Pattern Recognition
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
Spatial recognition and grouping of text and graphics
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
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The growing popularity of tablet PCs and intelligent pen-centric computing has increased the importance of freehand sketch recognition algorithms. In this paper, the proposed method integrates the temporal, spatial and geometric constraint information to improve the recognition accuracy. To interpret the sketch as an incremental process, the paper investigates the use of the information fusion technique with Support Vector Machines (SVMs) chain for modeling and understanding the spatial and temporal information of sketch sequences. Online sketch recognition is achieved through the use of the SVMs-chain for systematically modeling the dynamic and stochastic behaviors of the sketch. To validate its efficiency, the experimental results in various domains and the comparison with traditional Hidden Markov Models have been presented.