Farsi Handwritten Digits Recognition based on Interval Type-II Fuzzy Fusion of Support Vector Machines

The problem of automatic handwritten context recognition has received considerable attention of many researchers. In this paper, a fusion system is proposed to enhance the recognition accuracy of Farsi handwritten digits. The proposed approach consists of a prepration process and two main phases. In the prepration process, some pre-processing operations are performed on the image. Then some features are extracted, among which a multi-objective particle swarm optimization selects more effective ones. For every image, these optimal features are given as the input data to the classifiers. In the first main phase, training datasets are used to construct three different SVMs. In order to achieve better results, the adaptive best-mass gravitational search algorithm is utilized to adjust the SVMs parameters. In the second main phase, an interval type–II fuzzy inference system receives the SVMs outputs and by combining them, it presents a more accurate estimation of the digit in the image. The results of applying the proposed approach to the problem of scanned Farsi handwritten digits in the standard HODA database demonstrated that this algorithm attains high accuracy, precision and recall performance indices, comparing to other existing methods.