Automatic Handwritten Digits Recognition (HDR) is the process of interpreting handwritten digits by machines. There are several approaches for handwritten digits recognition. In this paper we have proposed an appearance feature-based approach which process data using Histogram of Oriented Gradients (HOG). HOG is a very efficient feature descriptor for handwritten digits which is stable on illumination variation because it is a gradient-based descriptor. Moreover, linear SVM has been employed as classifier which has better responses than polynomial, RBF and sigmoid kernels. We have analyzed our model on MNIST dataset and 97.25% accuracy rate has been achieved which is comparable with the state of the art.

Very recently evolutionary optimization algorith ms use the Genetic Algorithm to imp rove the result of Optimizat ion problems. Several processes of the Genetic A lgorith m are based on 'Random', that is fundamental to evolutionary algorith ms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorith m to avoid local convergence. The experimental results show that the proposed method is much more efficient in comparison with the traditional Genetic Algorith m for solving optimization problems.

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