An investigation into the skeletonization approach of Hilditch
Pattern Recognition
An introduction to digital image processing
An introduction to digital image processing
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Drawing Order from Scanned Images of Multi-Stroke Handwriting
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Whiteboard scanning and image enhancement
Digital Signal Processing
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ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
The power of automatic feature selection: Rubine on steroids
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
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SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
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The sketch community has, over the past years, developed a powerful arsenal of recognition capabilities and interaction methods. Unfortunately, many people who could benefit from these systems lack pen capture hardware and are stuck drawing diagrams on traditional surfaces like paper or whiteboards. In this paper we explore bringing the benefits of sketch capture and recognition to traditional surfaces through a common smart-phone with the Sketch Practically Anywhere Recognition Kit (SPARK), a framework for building mobile, image-based sketch recognition applications. Naturally, there are several challenges that come with recognizing hand-drawn diagrams from a single image. Image processing techniques are needed to isolate marks from the background surface due to variations in lighting and surface wear. Further, since static images contain no notion of how the original diagram was drawn, we employ bitmap thinning and stroke tracing to transform the ink into the abstraction of strokes commonly used by modern sketch recognition algorithms. Since the timing data between points in each stroke are not present, recognition must remain robust to variability in both perceived drawing speed and even coarse ordering between points. We have evaluated Rubine's recognizer in an effort to quantify the impact of timing information on recognition, and our results show that accuracy can remain consistent in spite of artificially traced stroke data. As evidence of our techniques, we have implemented a mobile app in SPARK that captures images of Turing machine diagrams drawn on paper, a whiteboard, or even a chalkboard, and through sketch recognition techniques, allows users to simulate the recognized Turing machine on their phones.