International Conference on Circuits, Systems, Communications and Computers

Plenary Speech


Using Neural Networks-Based Optical Character Recognition Software to Analyze the Relationship of Ancient Scripts


Professor Peter Z. Revesz,
University of Nebraska-Lincoln,
Lincoln, USA

E-mail: revesz@cse.unl.edu

Abstract: This paper uses convolutional neural networks-based optical character recognition software for the novel application of analyzing the relationship of ancient scripts. The key idea is to first develop neural networks that learn to recognize each ancient script’s symbols with high accuracy. Second, when the symbols of one script are shown to a neural network that learned another script, then the “recognition” by the neural network of the foreign symbol can have a measurable strength. If the “recognition” strength is relatively high, then the foreign symbol and the “recognized” native symbol are likely cognates, indicating a common origin of the two script symbols. Our analysis method already yielded some surprising discoveries about the connections among ancient scripts, including the ancient Minoan Linear A script, which was prevalent in Bronze Age Crete.

Brief Biography of the Speaker: Peter Z. Revesz holds a Ph.D. degree in Computer Science from Brown University (dissertation title: Constraint Query Languages, advisor: Paris C. Kanellakis). He was a postdoctoral fellow at the University of Toronto before joining the University of Nebraska-Lincoln, where he is a professor in the Department of Computer Science and Engineering. He is an expert in databases, data mining and analytics, bioinformatics and computational linguistics. He is the author of Introduction to Databases: From Biological to Spatio-Temporal (Springer, 2010) and Introduction to Constraint Databases (Springer, 2002). Dr. Revesz held visiting appointments at the IBM T.J. Watson Research Center, INRIA, the Max Planck Institute for Computer Science, the University of Athens, the University of Hasselt, the U.S. Air Force Office of Scientific Research and the U.S. Department of State. He is a recipient of an AAAS Science & Technology Policy Fellowship, a J. William Fulbright Scholarship, an Alexander von Humboldt Research Fellowship, a Jefferson Science Fellowship, a National Science Foundation CAREER award, and a Faculty International Scholar of the Year award by Phi Beta Delta, the Honor Society for International Scholars.


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