Learning of fuzzy spatial relations between handwritten patterns

It is widely admitted that modeling 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 in measuring the correspondence between the relationship of two objects and a predefined model of spatial relation. Localization task consists in retrieving objects that are related to a reference object according to a predefined model of spatial relation. In this work, we introduce a new modeling 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 modeling outperforms commonly used relative positioning features.