Graph based Method for Online Handwritten Character Recognition Rabiaa Zitouni 1 a , Hala Bezine 2 and Najet Arous 1 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 Keywords: 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 recog- nition. 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 descrip- tion 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. 1 INTRODUCTION Graphs have emerged as an active area of research aims to model structural relations of objects and pat- terns. The graph’s ability to model different parts of an object as well as its bases on sound mathemati- cal background can be invested in many diverse fields (Baldini et al., 2019; Lee et al., 2018). In the domain of handwritten character recognition, graph drew the attention and whetted the interest of numerous re- searchers. The use of a graph-based handwritten recognition induces the need to formulate two main required oper- ations: transforming handwritten graphs into feature vectors and calculating the graph similarity. From this perspective, the common task is to compare graphs to find the similarities between them. This is known as graph matching (GM). Basically, two types of graph matching were adopted by researchers(Yan et al., 2016). The exact graph matching refers to the search for an exact replication of the test graph in the tem- plate graph as well as the conservation of all rela- tionships presented in test one. The complexity of the exact graph matching has not yet been speci- fied to be P or NP(Conte et al., 2004), but there are a https://orcid.org/0000-0002-7616-8374 polynomial algorithms for solving the isomorphism problem of certain graph categories. A well-known method is based on the depth-first search (backtrack- ing) with a forward checking method which greatly reduces the number of backtracking steps. The in- exact graph matching provides a distance value that indicates graph dissimilarity(Bengoetxea, 2002). One of the most flexible and versatile approaches to inex- act graph matching is graph edit distance(Abu-Aisheh et al., 2017). However, the latter suffers from its high complexity that limits its applicability to graphs with small size. For this reason, a number of methods ad- dressing the high computational complexity of graph edit distance computation has been established, e.g. (Darwiche et al., 2019). Moreover, in recent years many tree-based methods(Abu-Aisheh et al., 2015) have become of great interest to researchers since computational time and even the explored search space can be manage- able with the impact of the quality of the provided matching solution. Therefore, the primary motiva- tion of the paper lies in tree-based methods which can be explored in GM computation. Besides, owing to the variability and ambiguity of handwritten charac- ter strucure (for example: disorder, imprecision, con- nection, etc) the use of fuzzy graph-based description could be extremely helpful to add flexibility against Zitouni, R., Bezine, H. and Arous, N. Graph based Method for Online Handwritten Character Recognition. DOI: 10.5220/0008956602630270 In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP, pages 263-270 ISBN: 978-989-758-402-2; ISSN: 2184-4321 Copyright c  2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 263