Handwritten Recognition Using SVM, KNN and Neural Network

TL;DR

This paper compares the effectiveness of SVM, KNN, and Neural Network classifiers for handwritten recognition across various input sources like paper and touchscreens.

Contribution

It evaluates and contrasts three different classification methods for handwritten recognition, providing insights into their relative performance.

Findings

Neural Network outperforms SVM and KNN in accuracy.

KNN is faster but less accurate.

SVM offers a balance between speed and accuracy.

Abstract

Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touch-screens and other devices. In this paper we will using three (3) classification t o re cognize the handwritten which is SVM, KNN and Neural Network.