Graph Neural Networks Using Local Descriptions in Attributed Graphs: An Application to Symbol Recognition and Hand Written Character Recognition

Abstract

Graph-based methods have been widely used by the document image analysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation tools for processing these graph-based structural representations has always remained a hot topic of research. Recently, Graph Neural Network (GNN) have been used for solving different problems in the domain of document image analysis and recognition. In this article we take forward the state of the art by presenting a new approach to gather the symbolic and numeric information from the nodes and edges of a graph. We use this information to learn a Graph Neural Network (GNN). The experimentation on the recognition of handwritten letters and graphical symbols shows that the proposed approach is an interesting contribution to the growing set of GNN-based methods for document image analysis and recognition.

Keywords

• Graph Neural Networks (GNN)
• attributed graphs
• graph matching
• local descriptions
• graph similarity
• graph learning