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Authors: Jinpeng Li ; Christian Viard-Gaudin and Harold Mouchere

Affiliation: Université de Nantes, France

ISBN: 978-989-8425-79-9

Keyword(s): Unsupervised graphical symbol learning, Graph mining, Minimum description length principle, Online handwriting.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Concept Mining ; Context Discovery ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Generally, the approaches encountered in the field of handwriting recognition require the knowledge of the symbol set, and of as many as possible ground-truthed samples, so that machine learning based approaches can be implemented. In this work, we propose the discovery of the symbol set that is used in the context of a graphical language produced by on-line handwriting. We consider the case of a two-dimensional graphical language such as mathematical expression composition, where not only left to right layouts have to be considered. Firstly, we select relevant graphemes using hierarchical clustering. Secondly, we build a relational graph between the strokes defining an handwritten expression. Thirdly, we extract the lexicon which is a set of graph substructures using the minimum description length principle. For the assessment of the extracted lexicon, a hierarchical segmentation task is introduced. From the experiments we conducted, a recall rate of 84.2% is reported on the test par t of our database produced by 100 writers. (More)

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Paper citation in several formats:
Li, J.; Viard-Gaudin, C. and Mouchere, H. (2011). UNSUPERVISED HANDWRITTEN GRAPHICAL SYMBOL LEARNING - Using Minimum Description Length Principle on Relational Graph.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 164-170. DOI: 10.5220/0003637901720178

@conference{kdir11,
author={Jinpeng Li. and Christian Viard{-}Gaudin. and Harold Mouchere.},
title={UNSUPERVISED HANDWRITTEN GRAPHICAL SYMBOL LEARNING - Using Minimum Description Length Principle on Relational Graph},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={164-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003637901720178},
isbn={978-989-8425-79-9},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - UNSUPERVISED HANDWRITTEN GRAPHICAL SYMBOL LEARNING - Using Minimum Description Length Principle on Relational Graph
SN - 978-989-8425-79-9
AU - Li, J.
AU - Viard-Gaudin, C.
AU - Mouchere, H.
PY - 2011
SP - 164
EP - 170
DO - 10.5220/0003637901720178

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