Authors:
Rabiaa Zitouni
1
;
Hala Bezine
2
and
Najet Arous
1
Affiliations:
1
Laboratory LR-SITI ENIT, University Tunis El Manar, B.P.37, 1002 Tunis, Tunisia
;
2
Laboratory REGIM ENIS, University Sfax, B.P.1173, 3038 Sfax, Tunisia
Keyword(s):
Fuzzy Attributed Relational Graph, Graph Matching, Structural Pattern Recognition, Handwritten Graphs, Tree Search Method.
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 perfo
rmed 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.
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