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Handwritten gesture recognition driven by spatial context of strokes

François Bouteruche 1 Eric Anquetil 2, 3 Nicolas Ragot 4 
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
2 IntuiDoc - intuitive user interaction for document
Abstract : In this paper, we present a new approach that explicitly exploits the spatial context of strokes to drive the shape recognition. We call this recognition method “in-context recognition” (ICR). The underlying idea is that only a sub-set of all possible symbols can be recognized in a specific spatial context. The main challenge is to automatically detect and model the context areas of interest so that the recognition method can be independent of any specific information on the targeted pen-based application. The paper details the learning scheme of the ICR method and how the obtained model is used during the recognition process. The results on a real-world pen-based recognition problem show that the method can reach better performances than a classical approach by decreasing the shape recognition complexity.
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Conference papers
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Contributor : Nicolas Ragot Connect in order to contact the contributor
Submitted on : Wednesday, September 2, 2015 - 1:57:39 PM
Last modification on : Tuesday, January 11, 2022 - 5:56:22 PM


  • HAL Id : hal-01191720, version 1


François Bouteruche, Eric Anquetil, Nicolas Ragot. Handwritten gesture recognition driven by spatial context of strokes. 8th International Conference on Document Analysis and Recognition (ICDAR 2005), Aug 2005, Seoul, South Korea. pp.1221-1225. ⟨hal-01191720⟩



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