23 January 2012 Quantify spatial relations to discover handwritten graphical symbols
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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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Jinpeng Li, Jinpeng Li, Harold Mouchère, Harold Mouchère, Christian Viard-Gaudin, Christian Viard-Gaudin, } "Quantify spatial relations to discover handwritten graphical symbols", Proc. SPIE 8297, Document Recognition and Retrieval XIX, 82970F (23 January 2012); doi: 10.1117/12.910588; https://doi.org/10.1117/12.910588
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