{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:33:37Z","timestamp":1773156817005,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The objective of this work is to show the application of a Deep Learning algorithm able to operate the segmentation of ancient Egyptian hieroglyphs present in an image, with the ambition to be as versatile as possible despite the variability of the image source. The problem is quite complex, the main obstacles being the considerable amount of different classes of existing hieroglyphs, the differences related to the hand of the scribe as well as the great differences among the various supports, such as papyri, stone or wood, where they are written. Furthermore, as in all archaeological finds, damage to the supports are frequent, with the consequence that hieroglyphs can be partially corrupted. In order to face this challenging problem, we leverage on the well-known Detectron2 platform, developed by the Facebook AI Research Group, focusing on the Mask R-CNN architecture to perform segmentation of image instances. Likewise, for several machine learning studies, one of the hardest challenges is the creation of a suitable dataset. In this paper, we will describe a hieroglyph dataset that has been created for the purpose of segmentation, highlighting its pros and cons, and the impact of different hyperparameters on the final results. Tests on the segmentation of images taken from public databases will also be presented and discussed along with the limitations of our study.<\/jats:p>","DOI":"10.3390\/a16020079","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T05:33:53Z","timestamp":1675229633000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Tommaso","family":"Guidi","sequence":"first","affiliation":[{"name":"Institute of Applied Physics \u201cNello Carrara\u201d, National Research Council, Via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy"},{"name":"Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy"}]},{"given":"Lorenzo","family":"Python","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy"}]},{"given":"Matteo","family":"Forasassi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8534-7465","authenticated-orcid":false,"given":"Costanza","family":"Cucci","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics \u201cNello Carrara\u201d, National Research Council, Via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy"}]},{"given":"Massimiliano","family":"Franci","sequence":"additional","affiliation":[{"name":"Center for Ancient Mediterranean and Near Eastern Studies, CAMNES, Via del Giglio 13, 50123 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-4015","authenticated-orcid":false,"given":"Fabrizio","family":"Argenti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3759-7512","authenticated-orcid":false,"given":"Andrea","family":"Barucci","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics \u201cNello Carrara\u201d, National Research Council, Via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1021\/acs.jcim.9b00266","article-title":"Deep learning in chemistry","volume":"59","author":"Mater","year":"2019","journal-title":"J. Chem. Inf. 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