Copyright (c) 2023 Darmatasia Darmatasia
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Each article is copyrighted © by its author(s) and is published under license from the author(s).
When a paper is accepted for publication, authors will be requested to agree with the Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 Netherlands License.
Pengenalan Karakter Hieroglif Mesir Kuno Menggunakan Convolutional Neural Network
Corresponding Author(s) : Darmatasia Darmatasia
Jurnal INSYPRO (Information System and Processing),
Vol. 8 No. 1 (2023)
This research implements a Convolutional Neural Network (CNN) to recognize ancient Egyptian hieroglyphics. CNN is a deep learning architecture that automatically learns the features of data hierarchically. The CNN technique effectively integrates feature extraction and classifiers into one system. This study used hieroglyphic characters from the pyramid of Unas, which consisted of 170 kinds of characters, but this study only used 11 kinds of characters that had a sample size above 100, namely characters D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, and X1. The results showed that the accuracy achieved was 99%. This research is expected to help archaeologists, enthusiasts, tourists, and museum visitors to recognize hieroglyphic characters as historical objects that only a few people know.
Keywords: character recognition, ancient Egyptian hieroglyphics, convolutional neural network