This paper presents an affine invariant shape descriptor which could be applied to both binary and gray-level images. The proposed algorithm uses gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters  to obtain gradient information at different orientations. We aggregate the gradients into a shape signature. The signatures derived from rotated objects are shifted versions of the signatures derived from the original object. ne shape descriptor is defined as the Fourier transform of the signature. We also provide a distance definition for the proposed descriptor taking shifted property of the signature into account. The performance of the proposed descriptor is evaluated over a database containing license plate characters. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature.