Open AccessIntroduction of Balinese Script Handwriting Using Zoning and Multilayer Perceptron
Author(s) -
I Komang Arya Ganda Wiguna,
Agus Muliantara
Publication year - 2019
Publication title -
international journal of application computer science and informatic engineering
Language(s) - English
Resource type - Journals
ISSN - 2685-4600
DOI - 10.33173/acsie.34
Subject(s) - handwriting , normalization (sociology) , computer science , backpropagation , pattern recognition (psychology) , artificial neural network , artificial intelligence , centroid , character (mathematics) , feature extraction , mathematics , geometry , sociology , anthropology
Handwriting identification is one out of the many research ever conducted. In its development, the handwriting can be written in real time by the user by using the mouse (online character recognition). Various studies on the traditional character handwriting recognition continue to be developed. One of them is the recognition of the Balinese characters. Balinese characters have their own unique characters compared with the other regions. The difference between the shapes of the characters with the other characters are quite similar, or there are some characters that can only be distinguished by a small sketch or doodle.This study uses Artificial Neural Network with Backpropagation algorithm to perform the Balinese characters recognition and zoning as a method of feature extraction. In a variation of the extraction method, the characteristics used are Image Centroid and Zone (ICZ), Zone Centroid and Zone (ZCZ) and normalization of features. Of the three methods, it will be determined the best method used in the Balinese characters recognition.From the test results of the extraction method, the combined characteristics of the ICZ, ZCZ and normalization of features were the most effective to be used for the recognition of the Balinese characters. The level of accuracy obtained from the results of the online testing was 71,28% and 72,31% for offline testing, with parameters of Backpropagation, which used the value of learning rate of 0,03, a momentum value of 0,5 and the number of neurons in the hidden layer of 130.