Introduction to Project:
Handwritten digit recognition is the process to provide the ability to machines to recognize human handwritten digits. It is not an easy task for the machine because handwritten digits are not perfect, vary from person-to-person, and can be made with many different flavors. Among thousands of datasets available in the market, MNIST is the most popular dataset for enthusiasts of machine learning and deep learning. Above 60,000 plus training images of handwritten digits from zero to nine and more than 10,000 images for testing are present in the MNIST dataset. So, 10 different classes are in the MNIST dataset. The images of handwritten digits are shown as a matrix of 28×28 where every cell consists of a grayscale pixel value.
1. Deep Learning
2. Neural Networks
3. Tensorflow & Keras Libraries,Tkinter, Anaconda, Jupyter
Process followed for the project
1. Import libraries and dataset
2. The Data Preprocessing
3. Create the model
4. Train the model
5. Evaluate the model
6. Create GUI to predict digits
Prerequisites, who should this project?
Basic knowledge of deep learning with Keras library, the Tkinter library for GUI building, and Python programming are required to run this amazing project.