Multi-font Arabic word recognition using spectral features

We present a new technique for recognising Arabic cursive words from scanned images of text. The approach is segmentation-free, and is applied to four different Arabic typeface, where ligatures and overlaps pose challenges to segmentation-based methods. We first transform each word into a normalised polar image, then we apply a two dimensional Fourier transform to the polar image. The resultant spectrum tolerates variations in size and rotation of displacement. Each word is represented by a template that includes a set of Fourier coefficients. The recognition is based on a normalised Euclidean distance from those templates.