A neural network model for online handwritten mathematical symbol recognition

Thammano, Arit; Rugkunchon, Sukhumal

doi:10.1007/11816157_30

A neural network model for online handwritten mathematical symbol recognition

Author(s)

Thammano, Arit

Rugkunchon, Sukhumal

Date Issued

January 1, 2006

Type

Conference Paper

DOI

10.1007/11816157_30

Abstract

This paper proposes a new handwritten mathematical symbol recognition system that is flexible enough to let the users write the symbols in their own ways. They do not have to learn a completely new way of writing symbols. The proposed approach involves two main stages: online and offline. During the online stage, the input is classified into one of the four groups. During the offline stage, the new neural network, called Hausdorff ARTMAP, which is specifically designed for solving two dimensional binary pattern recognition problems is used to identify the symbols. The proposed model is tested in a writer independent mode using the researcher's own collected database. The result obtained is very encouraging. © Springer-Verlag Berlin Heidelberg 2006.

Citation

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 4113 LNCS - I, 292-298, 2006