Handwritten Digits Classification using a Convolutional Neural Network and Python

By Vivek Pandey

Implement a Convolutional Neural Network model to classify MNIST handwritten digit images using Keras and Python

CNN is basically a model known to be Convolutional Neural Network and in recent times it has gained a lot of popularity because of its usefulness. CNN uses multilayer perceptrons to do computational works. CNN uses relatively little pre-processing compared to other image classification algorithms. This means the network learns through filters that in traditional algorithms were hand-engineered. So, for the image processing task CNNs are the best-suited option

The MNIST database of handwritten digits has a training set of 60,000 examples and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to learn about various pattern recognition methods for real-world data while spending minimal effort on preprocessing and formatting.

We can get 99.09% accuracy by using CNN(Convolutional Neural Network) model.