Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network

Abstract

We propose a practical scheme for multilingual multi font, and multi size large set character recognition using self organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse classifier and LVQ4 language classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7320 different classes have been carried out on a 486 DX-2 66 MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse classifier, LVQ4 language classifiers, and LVQ4 fine classifiers has a high recognition rate of over 98.27% and a fast execution time of more than 40 characters per second.