hmm-based efficient sketch recognition
Current sketch recognition systems treat sketches as images or a collection of strokes, rather than viewing sketching as an interactive and incremental process. We show how viewing sketching as an interactive process allows us to recognize sketches using Hidden Markov Models. We report results of a user study indicating that in certain domains people draw objects using consistent stroke orderings. We show how this consistency, when present, can be used to perform sketch recognition efficiently. This novel approach enables us to have polynomial time algorithms for sketch recognition and segmentation, unlike conventional methods with exponential complexity.
efficiency sketch recognition sketching systems imaging collect stroke interaction incremental process hidden markov model user studies domain drawing object consistency order polynomial timing algorithm segmentation method complexity interpretation of user input enabling input technologies intelligent user interfaces