Handwritten digits classification from MNIST with TensorFlow on Android; Featuring Tutorial!

A tutorial for MNIST handwritten digit classification using sklearn, PyTorch and Keras.

🏆 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.

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).

Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!

Convolutional neural network model for handwritten digit classification, with dataset generator, and drawable input for new digits to test the models.

A complete solution for the MNIST handwritten digit classification challenge using PyTorch, including data exploration, model training, and Kaggle submission generation.

Simple Python neural network for handwritten digits classification in pure Numpy

An Improved LSTM Model for Behavior Recognition of Intelligent Vehicles.A total of four experiments have done,including vehivle behavior recognition experiment based on vehicle behavior data set from Udacity,handwritten digit recognition experiment based on handwritten number,emotion classification based on movie reviewm and next trajectory point regression fitting experiment based on NGSIM data set.

This project implements a Convolutional Neural Network (CNN) to recognize handwritten digits (0–9) using the MNIST dataset. The model is trained on labeled image data, achieving high accuracy in digit classification, and demonstrates the practical application of deep learning techniques in computer vision.”

✏️ An AI-driven web app for handwritten digit recognition using the MNIST dataset. It leverages TensorFlow for deep learning model training and Gradio to create an intuitive, interactive UI. Users can draw digits and receive instant predictions, showcasing practical AI deployment and real-time inference capabilities.

Simple MNIST Handwritten Digit Classification using Pytorch

Convolutional Neural Network (CNN) image classification of handwritten digits in Xilinx FPGA

Classification and Segmentation of the MNIST dataset given as a point set input. Classification: the program classifies hand written digits, given as a sample of 100 points in a 2 dimensional field. the architecture is based on a Stanford article of a PointNet which is especially efficient for 3D image classification. the PointNet classification accuracy is 92.86% Segmentation: this is an extension to the classification net which can later define segments within the pointset. the program receives an input of a handwritten digit, given as a sample of 200 points in a 2 dimensional field, where 100 of the points are a sample of the digit itself, and the rest of the points are "background" points which are not part of the digit. the program classifies each point into one of the 2 segments and returns if it is part of the digit or part of the background. the PointNet segmentation accuracy is 97.65%

Predicting the Handwritten Digit with the best Classification model choosen by KFold cross validation technique

Handwritten digit classification using scikit-learn digits dataset

Implementation of handwritten digit classification models trained on MNIST dataset and understanding the No Free Lunch Theorem by testing on USPS Dataset

Handwritten digit classification web app using Streamlit

:open_file_folder: Image classification, Farsi and Latin handwritten digit dataset

A novel model: Bayes-Neural Network is suggested and implemented to classify handwritten digits. Model is compared with traditional methods.

Detection and classification of handwritten digits in a video stream using OpenCV and Support Vector Machines (SVM)

This project uses pyside6 to package a QT program for pytorch-based MNIST handwritten digit classification, which is suitable for demonstrating convolutional neural networks in a cognitive course

Implementation of Handwritten digits classification from MNIST on Android using Keras and TensorFlow.

Classification of MNIST Handwritten Digits Database using Deep Learning

MNIST Handwritten Digit Classification and Recognition Using Convolutional Neural Network (CNN) Deep Learning

This repository introduces to my project "Handwritten-Digit-Classification" using MNIST Data-set . This project was implemented and executed by applying KNN algorithm with recognition accuracy of around 91-93 % . The desired results have been obtained by training the machine first using the mnist_train data-set and later testing the obtained results using mnist_test data-set , to recognise the handwritten digit.

TensorFlow Serving model for handwritten digits classification from MNIST.

Handwritten Digit Recognition is a deep learning project that uses a Convolutional Neural Network (CNN) to accurately identify digits (0–9) from handwritten images. It leverages the MNIST dataset to train and evaluate the model for real-time digit classification.

MNIST handwritten digit classification using PyTorch

Handwritten digits classification using nodejs and tensorflowjs.