There are 1 repository under handwritten-digits topic.
A simple, easy to use MNIST loader written in Python 3
MNIST Database of Handwritten Digits for MATLAB
Handwritten Number Recognition using CNN and Character Segmentation
Easy to use CMATERdb datasets converted in NumPy format
Trains a Neural Network to read handwritten digits (OCR). Uses synaptic for Node.js, socket.io and MongoDB
Multiple Handwritten Digit Recognition app Using Deep Learing - CNN from Canvas build on tkinter- GUI
Python implementation of a Yatzy score sheet detection using OpenCV, TensorFlow, MNIST
Handwritten digit classification web app using Streamlit
Digit recognition using SVM
Generate handwritten digits using Gans
Generation of Human-Like handwritten digits using different GAN Architectures. The models were developed using Low-Level Tensorflow.
MNIST handwritten digit classification using PyTorch
Handwritten Digits and Alphabets Recognition using Convolutional Neural Networks
A collection of 107,730 28x28 PNG files of digits from 0-9, with a dataset generator.
Using Multi Layer Perceptron to build the model. Classifies the handwritten digits of the MNIST database with around 98% accuracy.
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.
Famous MNIST dataset is used to build a simple model which can predict handwritten digits
Devnagari Handwritten Characters Dataset (DHCD)
A simple neural network for classifying handwritten digits
Udacity - Machine Learning Engineer Nanodegree Project - A Deep Neural Network to recognise handwritten digits
Neural Network to Detect hand written digits using python
Interactive Handwritten Digit Recognition: An intuitive Keras and TensorFlow powered app with Streamlit UI. Draw and predict digits in real-time.
This code utilizes a neural network to classify handwritten digits (0-9) from the MNIST dataset. Explore the process of data preparation, model training, and evaluation for accurate digit recognition.
Handwritten Bangla Numeric Digits Classification using ResNet-34. The work uses a subset of the BanglaLekha-Isolated dataset. For details, read the README file.
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
Figuring out which handwritten digits are most differentiated with PCA.