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Proceedings Paper

Quantify spatial relations to discover handwritten graphical symbols
Author(s): Jinpeng Li; Harold Mouchère; Christian Viard-Gaudin
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Paper 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.

Paper Details

Date Published: 24 January 2012
PDF: 8 pages
Proc. SPIE 8297, Document Recognition and Retrieval XIX, 82970F (24 January 2012); doi: 10.1117/12.910588
Show Author Affiliations
Jinpeng Li, Institut de Recherche en Communications et Cybernétique, CNRS, Univ. de Nantes (France)
Harold Mouchère, Institut de Recherche en Communications et Cybernétique, CNRS, Univ. de Nantes (France)
Christian Viard-Gaudin, Institut de Recherche en Communications et Cybernétique, CNRS, Univ. de Nantes (France)


Published in SPIE Proceedings Vol. 8297:
Document Recognition and Retrieval XIX
Christian Viard-Gaudin; Richard Zanibbi, Editor(s)

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