A Convolutional Neural Network model created using PyTorch library over the MNIST dataset to recognize handwritten digits .
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Updated
Jan 3, 2021 - Python
A Convolutional Neural Network model created using PyTorch library over the MNIST dataset to recognize handwritten digits .
The C++ neural network for handwritten digit recognition with online demo
This task of digit recognition is done using Hidden Markov Model using Speech signal as input. This model is written in C++ and is trained on custom-recorded digit dataset.
a Java project to classify images of digits
Uma interface que permite desenhar um digito e que reconhece o digito utilizando um algoritmo de machine learning.
A simple digit recognition project with MNIST dataset using Artifical Neural Networks.
Classification algorithms to identify images by their bitmaps
Implementation of MobileNet-V3 with pytorch and tested with Kaggle MNIST dataset
Digits Recognize with Drawing(HTML Canvas). The Machine Learning Model can recognize any Handwritten Digit.
Digit recognizer with 97.5% accuracy according to the Kagle scoring. I used different algorithms like; SVM, KNN, and Decision Tree
This is a project to solve SUDOKU using Computer Vision in python. Digit recognition model is trained on MNIST with accuracy>95%. Learned Machine Learning and Computer vision and implemented the concepts in this project.
user-drawn digit recognition program
Digit recognition using Tensorflow.js
MNIST Digit Recognition repository offers a robust solution for recognizing handwritten digits using the MNIST dataset.
Reconocedor de Dígitos trazados con el mouse
This script performs digit recognition by comparing an input image to a precomputed average and the digits dataset using Euclidean distance. The predicted digit is output, and results are saved to CSV files.
In this Kaggle competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.
This project includes a 2 layer neural network and a perceptron, both of which were trained for classifying faces and digits.
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