 Learning of fuzzy spatial relations between handwritten patternsAdrien Delaye and Eric Anquetil International Journal of Data Mining, Modelling and Management, 2014, vol. 6, issue 2, 127-147 Abstract: It is widely admitted that modelling of spatial information is very important for interpretation and recognition of handwritten expressions. Two distinct tasks have to be addressed by spatial models in this context. Evaluation task consists of measuring the correspondence between the relationship of two objects and a predefined model of spatial relation. Localisation task consists of retrieving objects that are related to a reference object according to a predefined model of spatial relation. In this work, we introduce a new modelling of relative spatial positioning that handles the two tasks under a unified framework and a training scheme for learning spatial models from data. The use of fuzzy mathematical morphology allows to deal with imprecision of positioning and to adapt to varying shapes of handwritten objects. Experimentations of the evaluation task over two datasets of online handwritten patterns prove that the proposed modelling outperforms commonly used relative positioning features. Keywords: spatial reasoning; fuzzy spatial relations; spatial model learning; handwriting recognition; fuzzy mathematical morphology; structural pattern recognition; handwritten patterns; modelling. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:6:y:2014:i:2:p:127-147 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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