Handwritten Digit Recognition by Support Vector Machine Optimized by Bat Algorithm

Handwritten digit recognition is an important but very hard practical problem. This is a classification problem for which support vector machines are very successfully used. Determining optimal support vector machine is another hard optimization problem that involves tuning of the soft margin and kernel function parameters. For this optimization we adjusted recent swarm intelligence bat algorithm. We intentionally used weak set of features, four histogram projections, to prove that even under unfavorable conditions our algorithm would achieve acceptable results. We tested our approach on standard MNIST benchmark datasets and compared the results with other recent approaches from literature where our proposed algorithm achieved better results i.e. higher correct classification percent.