IEEE Transactions on Pattern Analysis and Machine Intelligence
Temporal sketch recognition in interspersed drawings
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
Structuring and manipulating hand-drawn concept maps
Proceedings of the 14th international conference on Intelligent user interfaces
A visual approach to sketched symbol recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Robust classification of strokes with SVM and grouping
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
ChemInk: a natural real-time recognition system for chemical drawings
Proceedings of the 16th international conference on Intelligent user interfaces
Understanding, Manipulating and Searching Hand-Drawn Concept Maps
ACM Transactions on Intelligent Systems and Technology (TIST)
Spatial recognition and grouping of text and graphics
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
Parsing ink annotations on heterogeneous documents
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
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We present a framework for grouping and recognition of characters and symbols in online free-form ink expressions. The approach is completely spatial; it does not require any ordering on the strokes. It also does not place any constraints on the layout of the symbols. Initially each of the strokes on the page is linked in a proximity graph. A discriminative recognizer is used to classify connected subgraphs as either making up one of the known symbols or perhaps as an invalid combination of strokes (e.g. including strokes from two different symbols). This recognizer operates on the rendered image of the strokes plus stroke features such as curvature and endpoints. A small subset of very efficient image features is selected, yielding an extremely fast recognizer. Dynamic programming over connected subsets of the proximity graph is used to simultaneously find the optimal grouping and recognition of all the strokes on the page. Experiments demonstrate that the system can achieve 94% grouping/recognition accuracy on a test dataset containing symbols from 25 writers held out from the training process.