Followed book: Pattern Recognition and Image Analysis - Earl Gose, Richard Johnsonbaugh, Steve Jost
-
Updated
Mar 24, 2018 - Java
#
handwritten-digit-classification
Here are 9 public repositories matching this topic...
Devnagari Handwritten Characters Dataset (DHCD)
machine-learning handwritten-digits training-materials character-recognition handwriting-recognition nepali-numeral nepali-character-recognition handwritten-digit-classification handwritten-character-recognition devnagari-manuscript
-
Updated
Aug 21, 2018 - Jupyter Notebook
This repo contains deep learning projects for beginners.
nlp tensorflow keras neural-networks mnist-dataset seq2seq deeplearning neural-machine-translation transfer-learning cifar10 keras-tensorflow kaggle-dataset handwritten-digit-classification resnet18 bank-customer-churn potato-disease-classification tf-data-pipeline
-
Updated
Aug 1, 2023 - Jupyter Notebook
Handwritten Digit Recognition by MNIST
machine-learning keras mnist mnist-classification mnist-dataset matplotlib python-3 handwritten-digit-recognition keras-tensorflow mnist-classifier mnist-handwriting-recognition handwritten-digit-classification matplotlib-pyplot
-
Updated
Sep 18, 2022 - Python
This Jupyter Notebook demonstrates the classification of handwritten digits using a Logistic Regression model.
-
Updated
Aug 9, 2024 - Jupyter Notebook
Code samples for Handwritten digit classification using pixel, dissimilarity and digit's unique features
-
Updated
Apr 2, 2018 - MATLAB
This is an example for how handwritten digits can be learnt with random forests
machine-learning computer-vision random-forest machine-learning-algorithms supervised-learning handwritten-digits handwritten-numeral-recognition handwritten-digit-recognition random-forest-classifier random-forest-algorithm handwritten-digit-classification
-
Updated
Nov 30, 2017 - Jupyter Notebook
A CNN trained using Keras on the MNIST dataset to recognize Handwritten Digits
-
Updated
Apr 9, 2020 - Jupyter Notebook
This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.
deep-learning pytorch mnist mnist-classification convolutional-neural-networks gradio handwritten-digit-recognition softmax mnist-classifier cnns max-pooling multi-class-classification mnist-handwriting-recognition handwritten-digit-classification digit-recognition-mnist digit-classifier digit-classification gradio-interface
-
Updated
Oct 13, 2025 - Python
Improve this page
Add a description, image, and links to the handwritten-digit-classification topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the handwritten-digit-classification topic, visit your repo's landing page and select "manage topics."