Mathematical Symbol Indexing Using Topologically Ordered Clusters of Shape Contexts

This paper addresses the indexing and retrieval of mathematical symbols from digitized documents. The proposed approach exploits Shape Contexts (SC) to describe the shape of mathematical symbols. Starting from the vector space method, that is based on SC clustering, we explore the use of topological ordered clusters to improve the retrieval performance. The clustering is computed by means of Self-Organizing Maps that organize the clusters in two dimensional topologically ordered feature maps. The retrieval performance are compared with those obtained using the K-means clustering on a large collection of mathematical symbols gathered from the widely used INFTY database.