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

Crossing Experiences in Digital Epigraphy: From Practice to Discipline

Although a relevant number of projects digitizing inscriptions are under development or have been recently accomplished, Digital Epigraphy is not yet considered to be a proper discipline and there are still no regular occasions to meet and discuss. By collecting contributions on nineteen projects – very diversified for geographic and chronological context, for script and language, and for typology of digital output – this volume intends to point out the methodological issues which are specific to the application of information technologies to epigraphy. The first part of the volume is focused on data modelling and encoding, which are conditioned by the specific features of different scripts and languages, and deeply influence the possibility to perform searches on texts and the approach to the lexicographic study of such under-resourced languages. The second part of the volume is dedicated to the initiatives aimed at fostering aggregation, dissemination and the reuse of epigraphic materials, and to discuss issues of interoperability. The common theme of the volume is the relationship between the compliance with the theoretic tools and the methodologies developed by each different tradition of studies, and, on the other side, the necessity of adopting a common framework in order to produce commensurable and shareable results. The final question is whether the computational approach is changing the way epigraphy is studied, to the extent of renovating the discipline on the basis of new, unexplored questions

CyberResearch on the Ancient Near East and Eastern Mediterranean

CyberResearch on the Ancient Near East and Neighboring Regions provides case studies on archaeology, objects, cuneiform texts, and online publishing, digital archiving, and preservation. Eleven chapters present a rich array of material, spanning the fifth through the first millennium BCE, from Anatolia, the Levant, Mesopotamia, and Iran. Customized cyber- and general glossaries support readers who lack either a technical background or familiarity with the ancient cultures. Edited by Vanessa Bigot Juloux, Amy Rebecca Gansell, and Alessandro Di Ludovico, this volume is dedicated to broadening the understanding and accessibility of digital humanities tools, methodologies, and results to Ancient Near Eastern Studies. Ultimately, this book provides a model for introducing cyber-studies to the mainstream of humanities research

Semantic Domains in Akkadian Text

The article examines the possibilities offered by language technology for analyzing semantic fields in Akkadian. The corpus of data for our research group is the existing electronic corpora, Open richly annotated cuneiform corpus (ORACC). In addition to more traditional Assyriological methods, the article explores two language technological methods: Pointwise mutual information (PMI) and Word2vec.Peer reviewe

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Hypertext Semiotics in the Commercialized Internet

Die Hypertext Theorie verwendet die selbe Terminologie, welche seit Jahrzehnten in der semiotischen Forschung untersucht wird, wie z.B. Zeichen, Text, Kommunikation, Code, Metapher, Paradigma, Syntax, usw. Aufbauend auf jenen Ergebnissen, welche in der Anwendung semiotischer Prinzipien und Methoden auf die Informatik erfolgreich waren, wie etwa Computer Semiotics, Computational Semiotics und Semiotic Interface Engineering, legt diese Dissertation einen systematischen Ansatz für all jene Forscher dar, die bereit sind, Hypertext aus einer semiotischen Perspektive zu betrachten. Durch die Verknüpfung existierender Hypertext-Modelle mit den Resultaten aus der Semiotik auf allen Sinnesebenen der textuellen, auditiven, visuellen, taktilen und geruchlichen Wahrnehmung skizziert der Autor Prolegomena einer Hypertext-Semiotik-Theorie, anstatt ein völlig neues Hypertext-Modell zu präsentieren. Eine Einführung in die Geschichte der Hypertexte, von ihrer Vorgeschichte bis zum heutigen Entwicklungsstand und den gegenwärtigen Entwicklungen im kommerzialisierten World Wide Web bilden den Rahmen für diesen Ansatz, welcher als Fundierung des Brückenschlages zwischen Mediensemiotik und Computer-Semiotik angesehen werden darf. Während Computer-Semiotiker wissen, dass der Computer eine semiotische Maschine ist und Experten der künstlichen Intelligenz-Forschung die Rolle der Semiotik in der Entwicklung der nächsten Hypertext-Generation betonen, bedient sich diese Arbeit einer breiteren methodologischen Basis. Dementsprechend reichen die Teilgebiete von Hypertextanwendungen, -paradigmen, und -strukturen, über Navigation, Web Design und Web Augmentation zu einem interdisziplinären Spektrum detaillierter Analysen, z.B. des Zeigeinstrumentes der Web Browser, des Klammeraffen-Zeichens und der sogenannten Emoticons. Die Bezeichnung ''Icon'' wird als unpassender Name für jene Bildchen, welche von der graphischen Benutzeroberfläche her bekannt sind und in Hypertexten eingesetzt werden, zurückgewiesen und diese Bildchen durch eine neue Generation mächtiger Graphic Link Markers ersetzt. Diese Ergebnisse werden im Kontext der Kommerzialisierung des Internet betrachtet. Neben der Identifizierung der Hauptprobleme des eCommerce aus der Perspektive der Hypertext Semiotik, widmet sich der Autor den Informationsgütern und den derzeitigen Hindernissen für die New Economy, wie etwa der restriktiven Gesetzeslage in Sachen Copyright und Intellectual Property. Diese anachronistischen Beschränkungen basieren auf der problematischen Annahme, dass auch der Informationswert durch die Knappheit bestimmt wird. Eine semiotische Analyse der iMarketing Techniken, wie z.B. Banner Werbung, Keywords und Link Injektion, sowie Exkurse über den Browser Krieg und den Toywar runden die Dissertation ab

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Kodikologie und Paläographie im digitalen Zeitalter 2 - Codicology and Palaeography in the Digital Age 2

Der Einsatz digitaler Technik verändert den wissenschaftlichen Umgang mit der handgeschriebenen Überlieferung. Dieser Band vertieft Fragen zu Digitalisierung und Katalogisierung, zu automatischer Schrifterkennung und Schriftanalyse, und er erweitert eine Diskussion, die mit dem im letzten Jahr erschienenen ersten Band zur digitalen Handschriftenforschung angestossen worden ist: Welche Erkenntnisse können etwa naturwissenschaftliche Methoden liefern? Welche musik- und kunsthistorischen Fragestellungen lassen sich mit Hilfe moderner Informationstechnologien beantworten? Wie lassen sich Methoden einer digitalen Auswertung lateinischer Handschriften auf griechische, glagolithische oder ägyptische Texte anwenden? Der raum-zeitliche Rahmen der hier von einer internationalen Autorenschaft zusammengetragenen 22 wissenschaftlichen Beiträge reicht vom alten Ägypten bis ins Paris der Postmoderne. Mit Beiträgen von: Pádraig Ó Macháin; Armand Tif; Alison Stones, Ken Sochats; Melissa Terras; Silke Schöttle, Ulrike Mehringer; Marilena Maniaci, Paolo Eleuteri; Ezio Ornato; Toby Burrows; Robert Kummer; Lior Wolf, Nachum Dershowitz, Liza Potikha, Tanya German, Roni Shweka, Yacov Choueka; Daniel Deckers, Leif Glaser; Timothy Stinson; Peter Meinlschmidt, Carmen Kämmerer, Volker Märgner; Peter Stokes—Dominique Stutzmann; Stephen Quirke; Markus Diem, Robert Sablatnig, Melanie Gau, Heinz Miklas; Julia Craig-McFeely; Isabelle Schürch, Martin Rüesch; Carole Dornier, Pierre-Yves Buard; Samantha Saidi, Jean-François Bert, Philippe Artières; Elena Pierazzo, Peter Stokes. Einleitung von: Franz Fischer, Patrick Sahle. Unter Mitarbeit von: Bernhard Assmann, Malte Rehbein, Patrick Sahle

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas