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.

Devnagari Handwritten Characters Dataset (DHCD)

Followed book: Pattern Recognition and Image Analysis - Earl Gose, Richard Johnsonbaugh, Steve Jost

Handwritten Digit Recognition by MNIST

This repo contains deep learning projects for beginners.

This repository compares a CNNtention model with ResNet18 models and with other models in handwritten digit classification

This is an example for how handwritten digits can be learnt with random forests

Code samples for Handwritten digit classification using pixel, dissimilarity and digit's unique features

A CNN trained using Keras on the MNIST dataset to recognize Handwritten Digits

This Jupyter Notebook demonstrates the classification of handwritten digits using a Logistic Regression model.