Persian Handwritten Digits Recognition Using Zoning and Histogram Projection with Different Dimension of Feature Vector

In this paper, Persian handwritten digits reorganization using zoning features and projection histogram for extracting feature vectors with 21, 30, 69,105-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as a classifier. We tested presented algorithm on a subset of 8600 samples of the Hoda dataset that contained 80000 samples of Persian handwritten digits for performance analysis. Using 8000 samples in learning stage and another 600 samples in testing stage also the experiments have been performed on the entire data set. The results got with use of every three kernels of support vector machine and achieved maximum accuracy by using Gaussian kernel with gamma equal to 0.16. In preprocessing stage only image binarization is used and all the images of this dataset had been normalized at centers with size 40×40.The recognition rate, on the test datasets in order 91, 94.17, 97.83 and 98.67% was earned.