In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper3 proposed3 system3 for isolated handwritten3 3numeral recognition3 3using number3 of contours3, skeleton features, Number of watersheds, and ratio between the numbers of foreground3 3pixels in3 3upper half3 3part and3 3lower half-part3 3of the numerical digit image. Based on these features the present paper designed user-defined

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... classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k-nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results. The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The3 3novelty of3 3the proposed3 3method is3 3size 3invariant.