Digit recognition with tensorflow

Simple perceptron to recognize handwritten digits and application to demonstrate this ability

Real-time Handwritten Digit Recognition using Python and OpenCV.

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

This document explores the use of Principal Component Analysis (PCA) in a machine learning context, specifically for image classification using a dataset of numerical representations of digits. The dataset is loaded using sci-kit-learn's load_digits function, and initial exploration is conducted to understand its structure.

Simple Tensorflow model for recognizing digits, built over the MIST dataset

Convolutional neural network for handwritten digit classification

An implementation of a Multilayer Perceptron (MLP) neural network from scratch to recognize handwritten digits.

Digits detection with YOLOv8 detection model and ONNX pre/post processing

Application to scan exam cover pages and aggregate as well as analyze the exam's scores

This program will train several time an ai model in order to make it predict handwritten digits

The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.

Demo at

Project created on Computer Image Processing laboratories at Cracow University of Technology

A Python project that was developed as a university assignment for the subject of Signal Processing and Voice Recognition. The goal of this assignment was to make an ASR system that predict digits from a voice signal using Neural Network.

Handwritten digit recognizer AI.

Visualization of a neural net learning to recognise digtis