UNSUPERVISED HANDWRITTEN GRAPHICAL SYMBOL LEARNING - Using Minimum Description Length Principle on Relational Graph

Jinpeng Li, Christian Viard-Gaudin, Harold Mouchere

2011

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 part of our database produced by 100 writers.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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