Statistical geometric components of straight lines (SGCSL) feature extraction method for offline Arabic/Persian handwritten words recognition

In this study, the authors present a new feature extraction method for handwritten Arabic/Persian language word recognition. This feature is based on the angle, number, location, and size of straight lines which represents geometric and quantitative attributes of a word. At first, word image is broken into an m × n window and straight lines are extracted from each window. Then, the proposed features are taken from these lines and combined together. Finally, the features of the images are used for training and testing support vector machine classifier. The proposed method is tested on three datasets: IBN-SINA and IFN/ENIT for Arabic words and Iran-cities for Persian words recognition. Recognition accuracy of the proposed method is about 67.47, 86.22 and 80.78% for the Iran-cities, IBN-SINA and IFN/ENIT Arabic dataset, respectively, which is better than state-of-the-art methods.

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