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Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities

Abstract : In this paper we address both automatic recognition of sketched symbols and the construction of the corresponding models from user drawn examples. Our approach is based on a two stage process. In a first phase we use an Adjacency Grammar to express topological properties of the symbol. In order to be able to further disambiguate topologically similar configurations on the rules of the grammar that are triggered by the recognition process produce a set of local geometric invariants is defined. The combination of both steps results in an efficient recognition method for user drawn sketches. Furthermore, we show that the same approach can easily be adapted for the generation of Adjacency Grammars from user provided and hand drawn examples.
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Contributor : Bart Lamiroy <>
Submitted on : Saturday, October 6, 2007 - 7:00:04 AM
Last modification on : Tuesday, May 18, 2021 - 3:32:05 PM
Long-term archiving on: : Thursday, September 20, 2012 - 11:27:04 AM


  • HAL Id : inria-00104537, version 1



Joan Mas Romeu, Bart Lamiroy, Gemma Sánchez, Josep Lladós. Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities. 3rd Eurographics Workshop on Sketch-Based Interfaces and Modeling, European Association for Computer Graphics, Sep 2006, Vienne/Autriche. ⟨inria-00104537⟩



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