Pengenalan Karakter Hieroglif Mesir Kuno Menggunakan Convolutional Neural Network

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 networkPenelitian ini mengimplementasikan Convolutional Neural Network (CNN) untuk mengenali Hieroglif Mesir kuno. CNN adalah salah satu arsitektur deep learning yang secara otomatis mempelajari fitur pada sebuah data secara hierarki. CNN secara efektif mengintegrasikan ekstraksi fitur dan pengklasifikasi ke dalam satu sistem. Penelitian ini menggunakan karakter hieroglif dari piramida Unas yang terdiri dari 170 jenis karakter, namun penelitian ini hanya menggunakan 11 jenis karakter yang memiliki jumlah sampel di atas 100 yaitu karakter D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, dan X1. Hasil penelitian menunjukkan bahwa akurasi yang diperoleh mencapai 99%. Penelitian ini diharapkan dapat membantu arkeolog, peminat, turis, dan pengunjung museum untuk mengenali karakter atau tulisan hieroglif sebagai salah satu benda bersejarah yang hanya diketahui oleh beberapa orang saja. Kata Kunci: pengenalan karakter, hieroglif Mesir kuno, convolutional neural networ

Transferring Neural Representations for Low-dimensional Indexing of Maya Hieroglyphic Art

We analyze the performance of deep neural architectures for extracting shape representations of binary images, and for generating low-dimensional representations of them. In particular, we focus on indexing binary images exhibiting compounds of Maya hieroglyphic signs, referred to as glyph-blocks, which constitute a very challenging dataset of arts given their visual complexity and large stylistic variety. More precisely, we demonstrate empirically that intermediate outputs of convolutional neural networks can be used as representations for complex shapes, even when their parameters are trained on gray-scale images, and that these representations can be more robust than traditional handcrafted features. We also show that it is possible to compress such representations up to only three dimensions without harming much of their discriminative structure, such that effective visualization of Maya hieroglyphs can be rendered for subsequent epigraphic analysis

Multimedia Analysis and Access of Ancient Maya Epigraphy

This article presents an integrated framework for multimedia access and analysis of ancient Maya epigraphic resources, which is developed as an interdisciplinary effort involving epigraphers (someone who deciphers ancient inscriptions) and computer scientists. Our work includes several contributions: a definition of consistent conventions to generate high-quality representations of Maya hieroglyphs from the three most valuable ancient codices, which currently reside in European museums and institutions; a digital repository system for glyph annotation and management; as well as automatic glyph retrieval and classification methods. We study the combination of statistical Maya language models and shape representation within a hieroglyph retrieval system, the impact of applying language models extracted from different hieroglyphic resources on various data types, and the effect of shape representation choices for glyph classification. A novel Maya hieroglyph data set is given, which can be used for shape analysis benchmarks, and also to study the ancient Maya writing system

Automatic Maya Hieroglyph Retrieval Using Shape and Context Information

We propose an automatic Maya hieroglyph retrieval method integrating shape and glyph context information. Two re-cent local shape descriptors, Gradient Field Histogram of Orientation Gradient (GF-HOG) and Histogram of Orien-tation Shape Context (HOOSC), are evaluated. To encode the context information, we propose to convert each Maya glyph block into a first-order Markov chain and apply the co-occurrence of neighbouring glyphs. The retrieval results obtained based on visual matching are therefore re-ranked. Experimental results show that our method can significantly improve the glyph retrieval accuracy even with a basic co-occurrence model. Furthermore, two unique glyph datasets are contributed which can be used as novel shape bench-marks in future research