In this paper, we present a new technique for recognizing Arabic cursive words from scanned images of text. The approach is segmentation-free, and is applied to four different Arabic typefaces, where ligatures and overlaps pose challenges to segmentation-based methods. We transform each word into a normalized polar image, and then we apply a two-dimensional Fourier transform to the polar image. The resultant spectrum tolerates variations in size, rotation or displacement. A template that includes a set of Fourier coefficients represents each word. The recognition is based on a normalized Euclidean distance from those tem-plates.