Improving online handwritten mathematical expressions recognition with contextual modeling

Abstract : We propose in this paper a new contextual modelling method for combining syntactic and structural information for the recognition of online handwritten mathematical expressions. Those models are used to find the most likely combination of segmentation/recognition hypotheses proposed by a 2D segmentor. Models are based on structural information concerning the layouts of symbols. They are learned from a mathematical expressions dataset to prevent the use of heuristic rules which are fuzzy by nature. The system is tested with a large base of synthetic expressions and also with a set of real complex expressions
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https://hal.archives-ouvertes.fr/hal-00518458
Contributor : Harold Mouchère <>
Submitted on : Friday, September 17, 2010 - 1:02:11 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:03 PM

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Ahmad-Montaser Awal, Harold Mouchère, Christian Viard-Gaudin. Improving online handwritten mathematical expressions recognition with contextual modeling. International Conference on Frontiers in Handwriting Recognition, Nov 2010, India. pp.427 - 432, ⟨10.1109/ICFHR.2010.73⟩. ⟨hal-00518458⟩

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