An Intuitive Desktop GUI Application For Recognizing Multiple Handwritten Digits Drawn At The Same Time. Trained On MNIST Dataset and Built With Python, OpenCV and TKinter

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

RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.

本仓库包含了完整的深度学习应用开发流程，以经典的手写字符识别为例，基于LeNet网络构建。推理部分使用torch、onnxruntime以及openvino框架💖

It is a Python GUI in which you can draw a digit and the ML Algorithm will recognize what digit it is. We have used Mnist dataset

Virtual Pen + Recognition of handwritten digits

Simple english digit recognition GUI

Neural network/Back Propagation implemented from scratch for MNIST.从零开始实现神经网络和反向传播算法，识别MNIST

Recognize Handwritten Digits using Deep Learning on the browser itself.

A Naive Bayes hand-written number classifier implemented in Python using only built-in libraries. (MNIST dataset)

MLP, CNN, RBFN and SVM on MNIST dataset with Keras framework

Real time digit recognition using pygame and a pretrained keras model

Intro to TensorFlow tutorial code written for DigitalOcean

An MNIST dataset classifier implemented from scratch in NumPy.

Handwritten Digit Recognition using MNIST data base

This repository contains Python code for handwritten recognition using OpenCV, Keras, TensorFlow, and the ResNet architecture. The project utilizes two datasets: the standard MNIST 0-9 dataset and the Kaggle A-Z dataset. The OCR model is trained using Keras and TensorFlow, while OpenCV is used for image pre-processing.

Handwritten Digits Recognition using a Perceptron Neural Network

MNIST Classification using Neural Network and Back Propagation. Written in Python and depends only on Numpy

MNIST Live Detection using OpenCV, Tensorflow Lite and AutoKeras

Code for my tutorials on Artificial Neural Networks