Classification Libya cities database of optical handwriting recognition using MLP

Abstract

This paper aims to describe the process of classifying the Libyan Cities database for handwriting recognition using the Multi-Layer Perceptron (MLP) network, MLP consists of multiple layers of neural units. The Libyan cities data set for optical recognition of handwriting consists of 5000 digital samples of Libyan city names as binary images. The training data set was used to train the MLP model, and the test data set was used to evaluate the model’s performance, reaching an accuracy of 80.6%.