Handwritten Digit Recognition With Machine Learning Algorithms

Nowadays, the scope of machine learning and deep studies is increasing day by day. Handwriting recognition one examples in daily life for this field work. Data storage digital media a method that almost everyone using nowadays. At same time, it has become necessity people to store their notes even take directly environment. As solution need, applications have been developed can recognize numbers, characters, text from handwriting algorithms. Moreover, these convert them into visual characters. This project, investigated performance comparison algorithms commonly used which are more efficient. result study, accuracy was 98.66% with artificial neural network, 99.45% convolutional 97.05% K-NN, 83.57% Naive Bayes, 97.71% support vector 88.34% decision tree. study also system numbers similar mentioned applications. A desktop application interface end users show instant some allow experience system.