Deep Learning using the platform H2O and R

A guide for preprocessing handwritten digits using R

Implementation of a Convolutional Neural Network for Handwritten Digit Recognition

Easy to use CMATERdb datasets converted in NumPy format

Tensorflow implementation of Conditional GAN trained on MNIST dataset

Machine Learning and Neural Network techniques to recognize handwritten digits with high accuracy

Get and solve the handwriting dataset from MNIST

Interactive Handwritten Digit Recognition: An intuitive Keras and TensorFlow powered app with Streamlit UI. Draw and predict digits in real-time.

Neural Network to Detect hand written digits using python

Handwritten Digit Recognition is the capacity of a computer to interpret the manually written digits from various sources like messages, bank cheques, papers, pictures etc

This project offers a simple and intuitive interface for users to input text and generate images that showcase the text in a handwritten style. The generator supports several stylistic modifications including bold, underline, and color alterations, allowing users to create personalized and visually appealing images from their text.

A tutorial on image processing of handwritten digits.

Handwritten digits, a bit like the MNIST dataset.

Simple application for digit recognition with CNN using four different datasets

Figuring out which handwritten digits are most differentiated with PCA.

Achieved an accuracy of 90% in Handwritten Digit Recognition by implementing K-Nearest Neighbor(K-NN) algorithm on MNIST dataset (a database of several handwritten digits ) to recognize any handwritten digit.

Recognition of handwritten digits using neural networks (From scratch)

Deep Neural Network for MNIST Classification

Generate handwritten digits using Gans

Learns using the input from the testing_data how to compute the output