Human Verification Using MNIST Dataset (with Code)
Introduction In this paper, we will classify the handwritten digits using a multilayer neural network. We will use this classification to build the human verification system as we ask humans to write a 3-digit number and check if written correctly and validate the number entered by the user. As they are many ways to write some digits and they can be written anywhere in the box we use open CV to get the perfect size image and use ML to predict the number and their using JavaScript we verify the number. For the prediction, we are using a 3 hidden layer neural network. In the MNIST dataset, we get a 28*28 size image dataset in which each is surrounded by 4 pixels in every direction. We have achieved a 97.23% success rate of the classification of digits from the MNIST dataset. GitHub Live Demo Data Analysis and Visualization MNIST data consists of 70,000 handwritten digit images. We will follow the steps from preprocessing to predicting the digit. We will start by understanding how an ima...