There are 0 repository under digit-classification topic.
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
Simple MNIST Handwritten Digit Classification using Pytorch
Kaggle Top 4% Project. CNN Based high precise MNIST like Kannada digit recognizer
In this part, we developed an interface for Digit Classification using the PyQt5 library in Python.
A "Hello World" ML neural network project features a FastAPI docker image for digit predictions and a React frontend where users can draw digits to see instant predictions
TensorFlow2 digits classification - Linear Classifier and MLP
Workshops
This project uses autoencoders to denoise MNIST images, aiming to improve handwritten digit recognition by refining classifier training data
A simple project that detects handwritten digits with keras
Classification of digits based on their Audio Inputs.
Making Neural network model from scratch for prediction of digit classification. Its built from scratch using feedforward and backpropagation loops using numpy arrays.
This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting.
Code and data for the Digit Recognizer competition on Kaggle.
The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different classifier model
Implement digit classification using LibSVM libarary.
Web interface to a Convolutional Neural Network (CNN) for classifying handwritten digits
Designd a ML Model from the MNIST dataset to identify digit classification using the SVM algorithm.
Handwritten Digit Classification on MNIST Dataset, Utilising Only Traditional Machine Learning Techniques and a Custom Feature Extractor, achieving highest accuracy of 98.08% with the same.
A simple neural network emulator of a multilayer perceptron without using external library methods for classification digits (0, 1, 2 and 3) represented as an array of 0 and 1.
The MNIST dataset was used to train a neural network having a single linear layer with SoftMax employed in the criterion function (Cross Entropy Loss) to classify handwritten digits in classes 0 to 9. The model yielded a 92% accuracy on the MNIST test dataset in 10 training epochs.
Digit classification task using Naive Bayes, Perceptron, and MIRA.
I have implemented a Conv2d algo to classify the hand made digits data which can be found on Kaggle . Got an accuracy of 99.76. To download the data for this model go to https://www.kaggle.com/c/digit-recognizer
Welcome to the Digit Classification Project! This project focuses on training a model to classify handwritten digits and using the trained model to predict digits from new images.
A GUI written in C++ in Ubuntu18. Draw a digit and see the recognition result. Training: k-means extracts patch features + PCA + fc layer + cost + SGD training.
Draw Digits to auto recognise them
Scene Classification
It is about implementing KNN(K nearest neighbor) on Mnist dataset which contains digit images
4th Year Emerging Technologies Project
In this project, I use Keras and TensorFlow to classify digits and python's Tkinter library to visualize