Graph based Method for Online Handwritten Character Recognition

Rabiaa Zitouni, Hala Bezine, Najet Arous

2020

Abstract

In this research, we attempt to propose a novel graph-based approach for online handwritten character recognition. Unlike the most well-known online handwritten recognition methods, which are based on statistical representations, we set forward a new approach based on structural representation to overcome the inherent deformations of handwritten characters. An Attributed Relational Graph (ARG) is dedicated to allowing the direct labeling of nodes (strokes) and edges (relationships) of a graph to model the input character. Each node is characterized by a set of fuzzy membership degrees describing their properties (type, size). Fuzzy description is invested in order to guarantee more robustness against uncertainty, ambiguity and vagueness. ARGs edges stand for spatial relationships between different strokes. At a subsequent stage, a tree-search based optimal matching algorithm is explored, which allows the search for character structures i.e the minimum cost of nodes. Experiments performed on ADAB and IRONOFF datasets, reveal promising results. In particular, the comparison with the state of the art demonstrates the significance of the proposed system.

Paper Citation

in Harvard Style

Zitouni R., Bezine H. and Arous N. (2020). Graph based Method for Online Handwritten Character Recognition. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP; ISBN 978-989-758-402-2, SciTePress, pages 263-270. DOI: 10.5220/0008956602630270

in Bibtex Style

@conference{grapp20,
author={Rabiaa Zitouni and Hala Bezine and Najet Arous},
title={Graph based Method for Online Handwritten Character Recognition},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP},
year={2020},
pages={263-270},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008956602630270},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP
TI - Graph based Method for Online Handwritten Character Recognition
SN - 978-989-758-402-2
AU - Zitouni R.
AU - Bezine H.
AU - Arous N.
PY - 2020
SP - 263
EP - 270
DO - 10.5220/0008956602630270
PB - SciTePress