Handwritten Arabic word recognition based on Ridgelet transform and support vector machines

We propose a method for handwritten Arabic word recognition based on the combination of the Ridgelet transform and SVMs. Ridgelets are used for generating pertinent features of handwritten words while the classification stage is based on the One-Against-All multiclass implementation of SVMs. The experimental investigation is conducted on a vocabulary of twenty-four words extracted from the ITN/ENIT database. The Ridgelet performance is assessed comparatively to the results obtained for Radon and uniform grid (zoning) features. Results highlight the reliability of the Ridgelet-SVM combination for handwritten Arabic word recognition.