Convert typed text to realistic handwriting!

Alphabet recognition using EMNIST dataset for humans ⚓

✍️ Convolutional Recurrent Neural Network in Pytorch | Text Recognition

Handwriting recoginition program made using CNN in Python.

Teaching a neural network how to write letters and digits with reinforcement learning.

This is a simple app to predict the alphabet that is written on the screen using an object of interest.

generate arbitrary handwritten letter/digits based on the inputs

Digits Recognizer using correlation and similarity methods in MNIST Letters dataset.

EMNIST Character Recognizer : Draw a character, and the app instantly predicts it using a PyTorch model trained on EMNIST—all in a simple Tkinter interface.

Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.

A simple NN word recognizer based on the EMNIST dataset

Project 3 for Artificial Neural Networks

detecting hand written digits and letters from images (+camera) (EMNIST) (tensorflow)

Keras를 활용한 손글씨 교정 사이트 (‘20 제 14회 공개 SW 개발자 대회)

A modular neural network implemented from scratch in Python. Includes customizable architecture, training with backpropagation, evaluation metrics, and visualizations using the EMNIST dataset.

Simple parser of emnist letters

Natural Language Processing Model that can recognise handwritten letters and convert them to typed text.

Handwritten Character Recognition is a web application built with Flask that enables users to draw handwritten letters and receive real-time predictions using a deep learning model.

This is the code for my IB Extended Essay in Computer Science

Library to read the EMNIST and MNIST data sets