Examples of Tensorflow Lite on Android

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

Sample android application to detect digit drawn using TensorFlow & Firebase model.

This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.

MNIST Digit Recognition Using Scikit-Learn and Ensemble Learning and Then Make an Web-App Using Flask.

A handwritten digit classifier application with python, java, klotin and tensorflow dependencies

This is a machine learning project focused on accurately classifying handwritten digits from the popular MNIST dataset. It provides a collection of code and models that leverage different techniques to achieve high accuracy in recognizing and categorizing digits.

This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting.

FPGA-based TPU-style accelerator for handwritten digit classification. Implements a fully pipelined INT8 neural network on a Cyclone II FPGA, using a weight-stationary systolic MAC array to classify 16×16 MNIST digits in ~9.4 µs of pure compute time, with UART input and VGA output.

A simple digit classifier built using the MNIST dataset

This project encompasses a series of modules designed to facilitate the creation, training, and prediction using a PyTorch MLP Neural Network for digit classification based on the MNIST dataset.

Machine learner to classify a single handwritten digit on an image.

An implementation of multiclass perceptron as a digit recognizer

MATLAB digit classifier based on Andrew NG course

Train an neural network to identify digits written on a canvas

In this project, I use Keras and TensorFlow to classify digits and python's Tkinter library to visualize

A digit-classifier using CNN

An implementation showcasing the deployment of machine learning model onto the flask server with live demo deployed on AWS Lambda.