Handwritten Digits Recognition Using Particle Swarm Optimization

As humans, it is easy to recognize numbers, letters, voices, and objects, to name a few. However, making a machine solve these types of problems is a very difficult task. Handwritten digits recognition (HDR) is considered as one of difficult problems in the field of pattern recognition. Hence, evaluating a performance of other algorithms on HDR problem is of great importance. In this study, Particle Swarm Optimization (PSO) based method is exploited to recognize unconstrained handwritten digits. Each class is encoded as a centroid in multidimensional feature space and PSO is employed to probe the optimal position for each centroid. The algorithm evaluates on 5 folds cross validation of handwritten digits data, and the results reveal that PSO gives promising performance and stable behavior in recognizing these digits.