In the present work, we investigate the combination of Ridgelet transform and tangent similarities for handwritten Arabic word recognition. Ridgelets are used for generating pertinent features of handwritten words. These features are handled through tangent similarities to enforce the discrimination power by using a priori knowledge. The One-Against-All SVMs implementation is used for the classification stage. Experiments are conducted on a vocabulary of twenty-four words extracted from the IFN/ENIT database. In a first step, the Ridgelet performance is assessed comparatively to the results obtained for uniform grid (zoning) features. Thenafter, tangent similarities are computed to put the variability knowledge into Ridgelet features. Results showed that The combination of tangent similarities and Ridgelet features yields a robust descriptor, which improves the recognition accuracy while accelerating the runtime.