
Symbol detection in online handwritten graphics using Faster RCNN
Symbol detection techniques in online handwritten graphics (e.g. diagram...
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A Bayesian model for recognizing handwritten mathematical expressions
Recognizing handwritten mathematics is a challenging classification prob...
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Offline handwritten mathematical symbol recognition utilising deep learning
This paper describes an approach for offline recognition of handwritten ...
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Lake symbols for island parsing
Context: An island parser reads an input text and builds the parse (or a...
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Multiple ContextFree Tree Grammars: Lexicalization and Characterization
Multiple (simple) contextfree tree grammars are investigated, where "si...
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Determining Points on Handwritten Mathematical Symbols
In a variety of applications, such as handwritten mathematics and diagra...
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Gemini: A Grammar and Recommender System for AnimatedTransitions in Statistical Graphics
Animated transitions help viewers follow changes between related visuali...
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A General Framework for the Recognition of Online Handwritten Graphics
We propose a new framework for the recognition of online handwritten graphics. Three main features of the framework are its ability to treat symbol and structural level information in an integrated way, its flexibility with respect to different families of graphics, and means to control the tradeoff between recognition effectiveness and computational cost. We model a graphic as a labeled graph generated from a graph grammar. Nonterminal vertices represent subcomponents, terminal vertices represent symbols, and edges represent relations between subcomponents or symbols. We then model the recognition problem as a graph parsing problem: given an input stroke set, we search for a parse tree that represents the best interpretation of the input. Our graph parsing algorithm generates multiple interpretations (consistent with the grammar) and then we extract an optimal interpretation according to a cost function that takes into consideration the likelihood scores of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to structures defined in the grammar and it does not impose constraints present in some previous works (e.g. stroke ordering). By avoiding such constraints and thanks to the powerful representativeness of graphs, our approach can be adapted to the recognition of different graphic notations. We show applications to the recognition of mathematical expressions and flowcharts. Experimentation shows that our method obtains stateoftheart accuracy in both applications.
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