This paper proposes a comparison of three classification methods of graphical historical images. Historical image datasets are becoming bigger and bigger, and the use of classical computer vision techniques is not sufficient to deal with these large repositories. In the context of this paper, we propose to compare three methods by applying graph matching techniques on a dataset already used in many papers. The first one is based on a statistical approach, the second one on a graph-based classification, and finally the third one is an hybrid approach relying on the specificities of the two previous one. For this last method, we propose here to adapt it to this specific dataset. Some results are proposed and commented, what shows the superiority of the hybrid approach.
['Mickaël Coustaty', 'Jean-Marc Ogier']
Graph matching versus bag of graph: a comparative study for lettrines recognition