Abstract
Papyrology is the field of study dedicated to ancient texts written on papyri. One significant challenge faced by papyrologists and paleographers is the identification of writers, also referred to as scribes, who penned the texts preserved on papyri. Traditionally, paleographers relied on qualitative assessments to differentiate between writers. However, in recent years, these manual techniques have been complemented by computer-based tools that enable the automated measurement of various quantities such as letter height and width, character spacing, inclination angles, abbreviations, and more. Digital palaeography has emerged as a new approach combining advanced Machine Learning (ML) algorithms with high-quality digital images. This fusion allows for extracting distinctive features from the manuscripts, which can be utilized for writer classification using ML algorithms or Deep Learning (DL) systems. Integrating powerful computational methods and digital imagery has opened up new avenues in palaeography, enabling more accurate and efficient analysis of ancient manuscripts. After applying image processing and segmentation techniques, we exploited the power of Convolutional Neural Networks to characterize a scribe’s handwriting.
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Cilia, N.D. et al. (2024). A Novel Writer Identification Approach for Greek Papyri Images. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham. https://doi.org/10.1007/978-3-031-51026-7_36
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