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.

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

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

A simple project that detects handwritten digits with keras

Workshops

Code and data for the Digit Recognizer competition on Kaggle.

Classification of digits based on their Audio Inputs.

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

identify digits from MNIST dataset of tens of thousands of handwritten images

A GUI written in C++ in Ubuntu18. Draw a digit and see the recognition result. Training: k-means extracts patch features + PCA + fc layer + cost + SGD training.

The MNIST dataset was used to train a neural network having a single linear layer with SoftMax employed in the criterion function (Cross Entropy Loss) to classify handwritten digits in classes 0 to 9. The model yielded a 92% accuracy on the MNIST test dataset in 10 training epochs.

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

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