Effects of Feature Fusion on Improvement of Recognition Rate of Farsi Handwritten Digits

In this paper, feature fusion technique is employed for improvement of recognition of handwritten digits. By merging three different feature vectors, given a specific weight for each of vectors, the Genetic Algorithm and Particle Swarm Optimization processes were applied to calculate the optimum weights. The main objective in this study was to compare the calculated weights according to each of the optimization techniques to that of classifiers combination in order to achieve a higher recognition rate and time for Persian Handwritten digits. A database containing 60'000 training samples and 20'000 test samples is used for the process.