Handwritten Digit Recognition using Deep Neural Networks

People have never been extra reliant on machines than they're nowadays. As an instance, neural network and machine learning algorithms may be used to do everything to classify objects in films for adding some sound to silent films. Similarly, one of the many regions of studies and development is handwritten textual content reputation, which has a plethora of ability programs. Handwritten Recognition (HWR), many times also called as Transcribed textual content acknowledgment Handwritten Text Recognition (HTR), is the capability of a system to benefit and decipher comprehensible manually written input collected from various sources. On this paper, here used Convolution Neural network (CNN), support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) models to perform handwritten digit reputation on MNIST datasets. The main objective is to determine the quality possible model for digit recognition with the aid of evaluating the accuracy and execution instances of the aforementioned models. The CNN model was able to give 99.53% accuracy on testing data.