This manuscript considers a new approach based on two methods main swarm intelligence (SI), particle swarm optimization (PSO) and ant colony optimization (ACO) for solving recognition handwritten characters problem. We present an overview of the proposed approaches to be optimized and test on a number of handwritten characters in the experiments, as well. Experimental results show the higher degree performance of the proposed approaches. It is noted that a new approach in general generates an optimized comparison between the input samples and database samples which, improves the last recognition rate. Experimental results show that PSO algorithm outperforms ACO algorithm in convergence speed, coping with high dimensional spaces and straightforwardness with minimizing error recognition rate.