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

Simple MNIST Handwritten Digit Classification using Pytorch

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 "Hello World" ML neural network project features a FastAPI docker image for digit predictions and a React frontend where users can draw digits to see instant predictions

Kaggle Top 4% Project. CNN Based high precise MNIST like Kannada digit recognizer

TensorFlow2 digits classification - Linear Classifier and MLP

Binarization Digits of numbers and prepare digits for OCR.

This project uses autoencoders to denoise MNIST images, aiming to improve handwritten digit recognition by refining classifier training data

Workshops

A simple project that detects handwritten digits with keras

Code and data for the Digit Recognizer competition on Kaggle.

Making Neural network model from scratch for prediction of digit classification. Its built from scratch using feedforward and backpropagation loops using numpy arrays.

Classification of digits based on their Audio Inputs.

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

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

I have implemented a Conv2d algo to classify the hand made digits data which can be found on Kaggle . Got an accuracy of 99.76. To download the data for this model go to https://www.kaggle.com/c/digit-recognizer

Draw Digits to auto recognise them

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