Ring-projection-wavelet-fractal signatures: a novel approach to feature extraction

In this brief, we present a novel approach to optical character recognition that utilizes ring-projection-wavelet-fractal signatures (RP-WFS). In particular, the proposed approach reduces the dimensionality of a 2-D pattern by way of a ring-projection method and, thereafter, performs Daubechies' wavelet transformation on the derived 1-D pattern to generate a set of wavelet transformation subpatterns, namely, curves that are nonself-intersecting. Further, from the resulting nonself-intersecting curves, the divider dimensions are readily computed. These divider dimensions constitute a new feature vector for the original 2-D pattern, defined over the curves' fractal dimensions. We have conducted several experiments in which a set of printed alphanumeric symbols of varying fonts and orientation were classified, based on the formulation of our new feature vector. The results obtained from these experiments have consistently shown the character recognition approach with the proposed feature vector can yield an excellent classification rate of 100%.