Design Of Cnn Based Model For Handwritten Digit Recognition Using Different Optimizer Techniques

Abstract

Convolutional Neural Network (CNN) is the well-known technique for feature extraction capability. But the poor selection and setup of design parameters restricts its performance during the training. The necessity of effective CNN design is required in various fields such as banking, security and in digital documentation to recognize the specific handwritten pattern. In this direction, we design a custom CNN model to precisely recognize the handwritten digit using different set of optimizers. The behavior of the presented approach has been experimented on the public MNIST dataset. The results show the effectivity of the model outperforms several state-of-the-art techniques in the presented field.