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

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