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Published in:
Computer Graphics for Archaeology Read chapter Read first chapter

2020 | OriginalPaper | Chapter

7. Classification and Detection of Symbols in Ancient Papyri

Authors : Alexandros Haliassos, Panagiotis Barmpoutis, Tania Stathaki, Stephen Quirke, Anthony Constantinides

Published in: Visual Computing for Cultural Heritage

Publisher: Springer International Publishing

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Abstract

The advent of powerful image processing and machine learning tools has generated opportunities for more automated and efficient paleography. In this work, methods were developed to automate aspects of the transcription process of ancient papyri. First, a technique is proposed that employs color thresholding and contouring for the automatic extraction of symbols from papyri photographs. Second, two symbol classifiers are considered: a support vector machine that intakes Histogram of Oriented Gradients features, and a convolutional neural network. Finally, a novel system is described based on the sliding window approach that limits the number of windows to be considered and uses a combination of a support vector machine and a convolutional neural network to benefit from both accurate and fast detections. The performance of these methods was evaluated on a set of papyri photographs from the Petrie Museum of Egyptian Archaeology.

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previous chapter Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance Analysis
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Metadata
Title
Classification and Detection of Symbols in Ancient Papyri
Authors
Alexandros Haliassos
Panagiotis Barmpoutis
Tania Stathaki
Stephen Quirke
Anthony Constantinides
Copyright Year
2020
Book
Visual Computing for Cultural Heritage

Print ISBN: 978-3-030-37190-6

Electronic ISBN: 978-3-030-37191-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37191-3_7