Aim: To enhance the Accuracy in Handwritten Character using Novel Open Scale invariant feature transform (SIFT) and Convolutional Neural Network (CNN). Materials and Methods: This study contains 2 groups i.e Novel Open Scale Invariant Feature Transform and Convolutional Neural Network. Each group consists of a sample size of 20 and the study parameters include alpha value 0.05, beta value 0.2 and the power value 0.8. Their accuracies are compared with each other using different sample sizes also. Results: The Novel Open Scale Invariant Feature Transform is 96.97% more accurate than the Convolutional Neural Network of 94.33% in Handwritten Character Recognition. Conclusion: The SIFT model is significantly better than the CNN in Enhancing the Accuracy in Handwritten Character.