It is a Python GUI in which you can draw a digit and the ML Algorithm will recognize what digit it is. We have used Mnist dataset

In this part, we developed an interface for Digit Classification using the PyQt5 library in Python.

Building a Neural Network for MNIST Digit Classification from Scratch

A Django-based web platform that hosts multiple image classification models under one unified interface. Upload an image and get the predicted result instantly.

A numpy implementation of the LeNet-CNN 1998 research paper trained on emnist dataset

In this project, I use Keras and TensorFlow to classify digits and python's Tkinter library to visualize

Digit Classification using Python

Digit classification task using Naive Bayes, Perceptron, and MIRA.

This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.

Implement digit classification using LibSVM libarary.

Utilizing neural networks to recognize handwritten digits.

This project implements probabilistic machine learning methods, including Bayesian classification, Gaussian discriminant models, and dropout in neural networks. It explores softmax regression, log-likelihood optimization, and performance evaluation using accuracy, ROC curves, and confusion matrices.