Extraction of fractal feature for pattern recognition

An approach to feature extraction based on fractal theory is presented as a powerful technique in pattern recognition. It can be used to extract the features of 2D objects, and identify different scripts. A fractal feature and fractal signature are reported. A multiresolution family of the wavelets is also used to compute information conserving micro-features. We employed a central projection method to reduce the dimensionality of the original input pattern, and a wavelet transformation technique to transform the derived pattern into a set of sub-patterns, from which the fractal dimension can readily be computed. Moreover, we have proposed an approach to classify different language using the modified fractal signature. For all these cases, difference in fractal dimension can yield the significative values. We expect that the proposed fractal method can also be used for improving the extraction and classification of features in a pattern recognition system