The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Interactive sketching for the early stages of user interface design
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Interpreting Sloppy Stick Figures by Graph Rectification and Constraint-Based Matching
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Sim-U-Sketch: a sketch-based interface for SimuLink
Proceedings of the working conference on Advanced visual interfaces
Sketched Symbol Recognition using Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Recognition and Grouping of Handwritten Text in Diagrams and Equations
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Perceptually based learning of shape descriptions for sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Pen-based styling design of 3D geometry using concept sketches and template models
Proceedings of the 2006 ACM symposium on Solid and physical modeling
Sketch Interpretation Using Multiscale Models of Temporal Patterns
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2007 courses
CodeAnnotator: digital ink annotation within Eclipse
OZCHI '07 Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces
Sketch recognition in interspersed drawings using time-based graphical models
Computers and Graphics
Using Error Recovery Techniques to Improve Sketch Recognition Accuracy
Graphics Recognition. Recent Advances and New Opportunities
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
Free hand sketch understanding using SVMs-chain modeling for spatial and temporal patterns
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Recognition of hand drawn chemical diagrams
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Freely-drawn sketches interpretation using SVMs-chain modeling
Engineering Applications of Artificial Intelligence
Constellation models for sketch recognition
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Parsing ink annotations on heterogeneous documents
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Supervised machine learning for grouping sketch diagram strokes
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
vsInk: integrating digital ink with program code in visual studio
AUIC '13 Proceedings of the Fourteenth Australasian User Interface Conference - Volume 139
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We present a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. The approach is completely spatial, that is it does not require any ordering on the strokes. It also does not place any constraint on the relative placement of the shapes or symbols. Initially each of the strokes on the page is linked in a proximity graph. A discriminative classifier is used to classify connected subgraphs as either making up one of the known symbols or perhaps as an invalid combination of strokes (e.g. including strokes from two different symbols). This classifier combines the rendered image of the strokes with stroke features such as curvature and endpoints. A small subset of very efficient features is selected, yielding an extremely fast classifier. An A-star search algorithm over connected subsets of the proximity graph is used to simultaneously find the optimal segmentation and recognition of all the strokes on the page. Experiments demonstrate that the system can achieve 97% segmentation/recognition accuracy on a cross-validated shape dataset from 19 different writers.