Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Recognizing multistroke geometric shapes: an experimental evaluation
UIST '93 Proceedings of the 6th annual ACM symposium on User interface software and technology
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
QuickSet: multimodal interaction for distributed applications
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
A New Pattern Representation Scheme Using Data Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Learning for Approximation in Stochastic Processes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Sketch based interfaces: early processing for sketch understanding
Proceedings of the 2001 workshop on Perceptive user interfaces
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
Generative Models and Bayesian Model Comparison for Shape Recognition
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
Proceedings of the 11th international conference on Intelligent user interfaces
Sketch Interpretation Using Multiscale Models of Temporal Patterns
IEEE Computer Graphics and Applications
Properties of Real-World Digital Logic Diagrams
PLT '07 Proceedings of the First International Workshop on Pen-Based Learning Technologies
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Sketch interpretation and refinement using statistical models
EGSR'04 Proceedings of the Fifteenth Eurographics conference on Rendering Techniques
Spatial recognition and grouping of text and graphics
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
Constellation models for sketch recognition
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
SketchML a representation language for novel sketch recognition approach
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Iconic and multi-stroke gesture recognition
Pattern Recognition
An Agent-Based Paradigm for Free-Hand Sketch Recognition
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
ChemInk: a natural real-time recognition system for chemical drawings
Proceedings of the 16th international conference on Intelligent user interfaces
From engineering diagrams to engineering models: Visual recognition and applications
Computer-Aided Design
Technical Section: Neural network-based symbol recognition using a few labeled samples
Computers and Graphics
A new paradigm based on agents applied to free-hand sketch recognition
Expert Systems with Applications: An International Journal
Sketched symbol recognition with auto-completion
Pattern Recognition
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Sketching is a natural mode of interaction used in a variety of settings. With the increasing availability of pen-based computers, sketch recognition has gained attention as an enabling technology for natural pen-based interfaces. Previous work in sketch recognition has shown that in certain domains the stroke orderings used when drawing objects contain temporal patterns that can aid recognition. So far, systems that use temporal information for recognition have assumed that objects are drawn one at a time. This paper shows how this assumption can be relaxed to permit temporal interspersing of strokes from different objects. We describe a statistical framework based on dynamic Bayesian networks that explicitly models the fact that objects can be drawn interspersed. We present recognition results for hand-drawn electronic circuit diagrams, showing that handling interspersed drawing provides a significant increase in accuracy.