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. Indexed symbols are represented with a vector space-based method that is grounded on SC clustering. We explore the use of the Self Organizing Map (SOM) to perform the clustering and we compare several approaches to compute the SCs. The retrieval performance are measured on a large collection of mathematical symbols gathered from the widely used INFTY database.
Mathematical Symbol Indexing / S. Marinai; B. Miotti; G. Soda. - STAMPA. - LNAI 5883 Springer Verlag:(2009), pp. 102-111. (Intervento presentato al convegno XIth International Conference of the Italian Association for Artificial Intelligence tenutosi a Reggio Emilia (Italy) nel December 9-12, 2009) [10.1007/978-3-642-10291-2_11].
Mathematical Symbol Indexing
MARINAI, SIMONE;MIOTTI, BEATRICE;SODA, GIOVANNI
2009
Abstract
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. Indexed symbols are represented with a vector space-based method that is grounded on SC clustering. We explore the use of the Self Organizing Map (SOM) to perform the clustering and we compare several approaches to compute the SCs. The retrieval performance are measured on a large collection of mathematical symbols gathered from the widely used INFTY database.
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