Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Abstract

Indonesia consists of various regional tribes, where each tribe has cultural diversity and some even have their own regional letters, like Javanese tribe has Javanese characters. Javanese letters consist of 20 basic letters called Nglegena script. Subject about Javanese language is delivered to elementary student until now aims to preserve Indonesian culture especially the Javanese. In this study, we present two feature extraction methods are Local Binary Pattern (LBP) and Profile Projection (PP). Neural Network (NN) chosen as classifiers for classifying 20 javanese letters Nglegena. Some digital image processing processes are carried out, are image inversion, dilation, denoising and skeletoning. The Javanese script dataset is taken from the Kaggle database with the name Aksara Jawa: Aksara Jawa Custom Dataset, consists of 2154 train images and 480 test images. The experiment were carried out in two models, Projection Profile Histogram - Neural Network (PPH-NN) and Local Binary Pattern Histogram - Neural Network (LBPH-NN). The experiment show that both feature extraction methods have very good performance, 99.98% PPH-NN and 89.6% LBPH-NN on average.