REVIEW ON DIGIT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Deep learning has recently taken a radical turn in the field of machine learning by making it more artificially intelligent, thanks to the advent of Convolutional Neural Networks (CNN). Neural networks are a group of algorithms that are identical to the human brain and are programmed to identify different patterns. A neural network learns from numerous levels of representation and reacts appropriately to different levels of abstraction where different patterns are learned by each layer. Handwritten Digit Recognition is an example of a computer's capacity to recognise human handwritten digits. Because handwritten numerals aren't flawless and might be generated with a variety of tastes, it's difficult work for the machine. In this paper, we use CNN to identify handwritten digits using different numbers of hidden layers and epochs to achieve highly accurate results. This research is carried out using the database of the Modified National Standards and Technology Institute (MNIST). This dataset was created using the convolutional neural network technique and Keras, a Python library for intensive computation of neural nodes that is supported by the Tensor Flow framework on the backend. We will be able to estimate the handwritten digits in an image using this model. This approach allows us to detect numerous digits.