Abstract
The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building text-shape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2% of strokes correctly classified compared with 86.9% and 83.3% for the two others.
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References
Rubine, D.H.: Specifying gestures by example. In: Proceedings of Siggraph ’91. ACM, New York (1991)
Paulson, B., Hammond, T.: PaleoSketch: Accurate Primitive Sketch Recognition and Beautification. In: Intelligent User Interfaces (IUI ’08). ACM Press, New York (2008)
Wobbrock, J.O., Wilson, A.D., Li, Y.: Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes. In: User interface software and technology. ACM, Newport (2007)
Plimmer, B.: Using Shared Displays to Support Group Designs; A Study of the Use of Informal User Interface Designs when Learning to Program. Computer Science (2004)
Young, M.: InkKit: The Back End of the Generic Design Transformation Tool. Computer Science (2005)
Schmieder, P., Plimmer, B., Blagojevic, R.: Automatic Evaluation of Sketch Recognition. In: Sketch Based Interfaces and Modelling, New Orleans, USA (2009)
Bhat, A., Hammond, T.: Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams. In: International Joint Conference on Artificial Intelligence (IJCAI ’09), Pasadena, California, USA (2009)
Bishop, C.M., Svensen, M., Hinton, G.E.: Distinguishing Text from Graphics in On-Line Handwritten Ink. In: Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition. IEEE Computer Society, Los Alamitos (2004)
Patel, R., Plimmer, B., et al.: Ink Features for Diagram Recognition. In: 4th Eurographics Workshop on Sketch-Based Interfaces and Modeling 2007. Eurographics, Riverside (2007)
Plimmer, B., Freeman, I.: A Toolkit Approach to Sketched Diagram Recognition. In: HCI 2007. eWiC, Lancaster (2007)
Lank, E., Thorley, J.S., Chen, S.J.-S.: An interactive system for recognizing hand drawn UML diagrams. In: Proceedings of the Centre for Advanced Studies on Collaborative research. IBM Press, Mississauga (2000)
Hammond, T., Davis, R.: Tahuti: A Geometrical Sketch Recognition System for UML Class Diagrams. In: 2002 AAAI Spring Symposium on Sketch Understanding (2002)
Zeleznik, R.C., Bragdon, A., et al.: Lineogrammer: creating diagrams by drawing. In: Proceedings of User interface software and technology. ACM, Monterey (2008)
Shilman, M., Viola, P.: Spatial recognition and grouping of text and graphics. In: EUROGRAPHICS Workshop on Sketch-Based Interfaces and Modeling (2004)
Shilman, M., Wei, Z., et al.: Discerning structure from freeform handwritten notes. In: Document Analysis and Recognition (2003)
Jain, A.K., Namboodiri, A.M., Subrahmonia, J.: Structure in On-line Documents. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition. IEEE Computer Society, Los Alamitos (2001)
Ao, X., Li, J., et al.: Structuralizing digital ink for efficient selection. In: Proceedings of the 11th international conference on Intelligent user interfaces. ACM, Sydney (2006)
Machii, K., Fukushima, H., Nakagawa, M.: Online text/drawings segmentation of handwritten patterns. In: Document Analysis and Recognition, Tsukuba Science City, Japan (1993)
Microsoft Corporation, Ink Analysis Overview (cited 2008), http://msdn.microsoft.com/en-us/library/ms704040VS85aspx
Mochida, K., Nakagawa, M.: Separating drawings, formula and text from free handwriting. In: International Graphonomics Society (IGS 2003), Scottsdale, Arizona (2003)
Blagojevic, R., Plimmer, B., et al.: A Data Collection Tool for Sketched Diagrams. In: Sketch Based Interfaces and Modeling. Eurographics, Annecy (2008)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Holmes, G., Pfahringer, B., et al.: Multiclass alternating decision trees. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 161–172. Springer, Heidelberg (2002)
Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees. Machine Learning 95(1-2), 161–205 (2005)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Stanford University (1998)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Platt, J.: Machines using Sequential Minimal Optimization. In: Advances in Kernel Methods - Support Vector Learning (1998)
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Blagojevic, R., Plimmer, B., Grundy, J., Wang, Y. (2010). Building Digital Ink Recognizers Using Data Mining: Distinguishing between Text and Shapes in Hand Drawn Diagrams. In: GarcÃa-Pedrajas, N., Herrera, F., Fyfe, C., BenÃtez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_36
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DOI: https://doi.org/10.1007/978-3-642-13022-9_36
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