In this paper, a novel 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 2-D objects, and identify different scripts. A new fractal feature and fractal signature is 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 dimensions can readily be computed. Moreover, we have proposed a new approach to classify different languages 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.