Feature extraction in character recognition based on wavelet-fractal method

In this paper, we are investigating the utility of several emerging techniques to extract features. A novel method of feature extraction is proposed, which includes utilizing the central projection transform to describe the shape, the wavelet transform to aid the boundary identification, and the fractal features to enhance image discrimination. In particular, this approach reduces the dimensionality of a 2-D pattern by way of a central projection method, and thereafter, performs Daubechies’ wavelet transform on the derived 1-D pattern to generate a set of wavelet transform sub-patterns, namely, curves that, are non-self-intersecting. The divider dimensions are readily computed from the resulting non-self-intersecting curve. These divider dimensions constitute a new feature vector for the original 2-D pattern, defined using the curves’ fractal dimensions. Once these feature vectors have been captured, we can compare them to the training set by calculating the Euclidean Distance between the different feature vectors. The smallest distance is considered to be the match.