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Conference papers

Quantifying spatial relations to discover handwritten graphical symbols

Jinpeng Li 1, * Harold Mouchère 1 Christian Viard-Gaudin 2
* Corresponding author
1 irccyn-ivc
IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
Abstract : To model a handwritten graphical language, spatial relations describe how the strokes are positioned in the 2-dimensional space. Most of existing handwriting recognition systems make use of some predefined spatial relations. However, considering a complex graphical language, it is hard to express manually all the spatial relations. Another possibility would be to use a clustering technique to discover the spatial relations. In this paper, we discuss how to create a relational graph between strokes (nodes) labeled with graphemes in a graphical language. Then we vectorize spatial relations (edges) for clustering and quantization. As the targeted application, we extract the repetitive sub-graphs (graphical symbols) composed of graphemes and learned spatial relations. On two handwriting databases, a simple mathematical expression database and a complex flowchart database, the unsupervised spatial relations outperform the predefined spatial relations. In addition, we visualize the frequent patterns on two text-lines containing Chinese characters.
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Contributor : Harold Mouchère <>
Submitted on : Monday, February 20, 2012 - 12:22:46 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:08 PM


  • HAL Id : hal-00672002, version 1



Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Quantifying spatial relations to discover handwritten graphical symbols. Document Recognition and Retrieval XIX, Part of the IS&T/SPIE 24th Annual Symposium on Electronic Imaging, Jan 2012, San Francisco, United States. ⟨hal-00672002⟩



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