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Understanding Sketches and Diagrams on the Tablet PC

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Understanding Sketches and Diagrams on the Tablet PC

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    1. Understanding Sketches and Diagrams on the Tablet PC Balaji Krishnapuram In collaboration with: Tablet PC Group (Redmond), and Collaborative Handwritten Ink Recognition Project group: Martin Szummer, Chris Bishop, Michel Gangnet, Markus Svensen

    2. Background Extensive work on recognizing hand-written text already Some problems remain, but works reasonably for the most part Much more to user interface than simply text!

    3. Project Objective: Assume the text has been separated from the figures in earlier pre-processing step Ongoing Research: Markus Svensen I focus on sketch and diagram understanding

    4. Practical Applications

    5. Understanding Figures: Subtasks Fitting: Identify best affine transformation of model for sample of ink

    6. Model for generating ink from templates

    7. Model for generating ink from templates

    8. Fitting algorithm

    9. Noise Immunity

    10. Fitting/Recognizing Segments:

    11. Segmentation: Wrapper Approach Stroke: from pen down to pen up Assume figures are drawn in a continuous sequence of strokes Assume existence of temporal ordering information i.e. S1, S2, S3, ..., ST Further assume that max. number of strokes used to draw a template, NS, is reasonably small (e.g. 10 or less)

    12. Segmentation: Divide & Conquer [score,partition]=f(S1, S2, S3, ..., ST , NS) Base case: if T< NS consider fitting/recognising the entire set of strokes as a single figure For all k=2 to T-1 : how good is it to divide it at k? [score1,partition1]=f(S1, S2, S3, ..., Sk , NS); [score2,partition2]=f(Sk+1, Sk+2, ..., ST , NS); Total_score(k)=score1+score2; Total_partition(k)=[partition1;partition2]; Return best score/partition out of all the possibilities considered.

    13. Square or 4 Lines?

    14. Over-explaining / Under-explaining

    15. Gets it right most of the time…

    16. … but some mistakes too

    17. Current limitations/problems Works fine most of the time! Mistakes when figures are confusingly close or very small Slow: Approx. 5 seconds for each of the previous figs. Each fitting takes about 0.1 seconds, combinatorial explosion in partitioning the image into segments We use information about temporal sequence of strokes! Temporal information lost during cut + paste operations Users do go back and add things to figures later Only considers Affine transform based fitting. Arrows and other complicated templates may need other (non-affine) fitting

    18. Further work: Scoring seems to be perfectly fine Main focus on partitioning the image: how to order the search through the set of all partitions, guaranteed to reach best interpretation eventually. Speed gains in fitting/recognizing individual figures Line based (instead of point based) Randomized algorithms like RANSAC (Phil, Antonio) Discriminative approach (feature extraction, learn classifiers for parallelograms, ellipses etc)

    19. Acknowledgements Martin Szummer, Chris Bishop, Michel Gangnet, Markus Svensen, Hannah Pepper Antonio Criminisi, Mike Tipping, Phil Torr The whole MLP group All those who provided us ink samples from real, human users!

    20. Questions / Suggestions !?!

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