Handwritten digit recognition is one of the most important problems in computer vision. There is a great interest in this area due to many potential applications, especially where a large number of documents must be analyzed, such as post mail sorting, bank check analysis and handwritten forms processing. Many approaches have been proposed with high recognition rates recently, however there is still room to increase the recognition accuracy because an error can be very costly in some applications. In this paper, a novel ensemble approaches are presented that involves bagged homogeneous classifier ensembles and arcing of heterogeneous ensembles. Then the classification performances of classifier models are assessed using accuracy. Here, classifier ensemble is built using base classifiers such as RBF and SVM. The feasibleness and the advantages of the proposed approaches are illustrated with the help of existing handwritten recognition dataset. The main originality of the proposed approach is based on three main parts: pre-processing phase, classification phase and combining phase. A broad series of analogous experiments are done for standard dataset of handwritten recognition. Furthermore, comparisons with previous work on standard dataset of handwritten recognition are also exhibited. The experimental outcomes demonstrate that this proposed ensemble approaches are competitive.