Handwritten Digit Recognition using Machine Learning and Deep Learning

A script that trains a model to recognize handwritten digits using the MNIST data set. Then it loads external files and uses the neural network to predict what digits they are.

Handwritten Digit Recognition using OpenCV, sklearn and Python

The MNIST database of handwritten digits is one of the most popular image recognition datasets. It contains 60k examples for training and 10k examples for testing.

Handwritten digit recognition with MNIST & Keras

Handwritten digit recognition using neural network trained on 60000 images from MNIST dataset

An image recognition/object detection model that detects handwritten digits and simple math operators. The output of the predicted objects (numbers & math operators) is then evaluated and solved.

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

NumberRecognition is a project aimed at recognizing handwritten digits from the MNIST dataset using PyTorch. It includes scripts for training and inference, along with utilities for dataset preparation. This project is ideal for learning the basics of neural networks and digit recognition. This is a "Hello World" program in the field of AI.

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

Handwritten Digit Recognition WebApp using pytorch and Flask

handwritten digit recognition system using a custom neural network architecture. Built with C, it features both training capabilities and an interactive recognition interface. >98% accuracy on the MNIST dataset.

✨ Draw handwritten digits and get instant AI predictions! Neural network implemented in pure JavaScript. Zero dependencies

基于OpenCV手写数字识别系统

Handwritten Digit Recognition Using Convolutional Neural Network by Python

Handwritten digit recognition in Elixir

A Flask web app for handwritten digit recognition using machine learning

Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning. In this project, we propose to implement the LeNet5 model using PyTorch to recognize handwritten digits from the MNIST dataset.

Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras on live camera

STM32F407ZGT6 Run BP Neural Network Handwritten Digit Recognition

🎰Handwritten digit recognition application implemented by TensorFlow2 + Keras and Flask.

Real time MNIST digit recognition with OpenCV and Support Vector Machine (SVM) algorithm.

Lenet for MNIST handwritten digit recognition using Vivado hls tool

This project demonstrates Handwritten digit recognition using Deep Learning

Handwritten digit recognition.

Verilog implementation of a pre-trained handwritten digit recognition simple neural network.

Recognize handwritten multi-digit numbers using a CRNN model trained with synthetic data.

An iOS App that recognizes handwritten digits using Swift and TensorFlow Lite

A handwirtten arabic numerals recognition