Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps

Published 17 December 2002

Jeremiah D. Deng, K.P. Chan, Yinglin Yu

DOI10.1109/icip.1994.413707

Citations23

TL;DR

A robust approach for recognition of text embedded in natural scenes by utilizing a local intensity normalization method to effectively handle lighting variations and using the linear discriminant analysis (LDA) for selection and classification of features.

Abstract

So far the bottleneck of Chinese recognition, especially handwritten recognition, still lies in the effectiveness of feature-extraction to cater for various distortions and position shifting. In the paper, a novel method is proposed by applying a set of Gabor spatial filters with different directions and spatial frequencies to character images, in an effort to reach the optimum trade-off between feature stability and feature localization. While a classic self-organizing map is used for unsupervised clustering feature codes, a multi-staged LVQ with a fuzzy judgement unit is applied for the final recognition on the feature mapping result.

Keywords

Computer Science

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