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Fuzzy Relative Positioning for On-Line Handwritten Stroke Analysis

François Bouteruche 1 Sébastien Macé 1 Eric Anquetil 1 
1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : This paper deals with the qualitative and robust modelling of the relative positioning of on-line handwritten strokes. We exploit the fuzzy approach to take the imprecision of such relations into account. We first transpose a well-formalized method which proved itself in the domain of image analysis to the on-line case; it aims at evaluating the relation “to be in a given direction” relatively to a reference. Our first contribution is a solution to deal with the particular nature of on-line strokes, which are constituted of non-connected points. Our second and main contribution is a method to learn automatically fuzzy relative position relationships. It aims at evaluating the relation “to be in a given position” relatively to a reference using jointly the direction and the distance. We test the impact of this new fuzzy positioning approach on one possible application: the recognition of handwritten graphic gestures, which requires spatial context information to be discriminated. Whereas the recognition rate is 52.95% without any spatial information, we obtain a maximum of 95.75% when we use learnt relative position relationships.
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Submitted on : Monday, October 9, 2006 - 12:33:50 PM
Last modification on : Thursday, February 7, 2019 - 3:08:56 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 7:00:55 PM


  • HAL Id : inria-00104760, version 1


François Bouteruche, Sébastien Macé, Eric Anquetil. Fuzzy Relative Positioning for On-Line Handwritten Stroke Analysis. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00104760⟩



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