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Egyptian Hieroglyphic Layout Analysis

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DataCite Commons2025-05-12 更新2025-04-15 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/C308X3
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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Ancient Egyptian Hieroglyphic Datasets</title> </head> <body> <section id="datasets"> <h2>Datasets for Ancient Egyptian Hieroglyphic Research</h2> <section id="hla-dataset"> <h3>Hieroglyphic Layout Analysis (HLA) Dataset</h3> <p><strong>Overview:</strong> The Hieroglyphic Layout Analysis (HLA) Dataset is a unique resource comprising <strong>897 high-resolution images</strong>, each containing multiple lines of Ancient Egyptian hieroglyphs and cartouches. This dataset is specifically designed for tasks related to identifying and segmenting the layout of hieroglyphic texts within artifact images.</p> <p><strong>Data Collection:</strong> The HLA Dataset was meticulously compiled from diverse sources:</p> <ul> <li><strong>Direct Collection (Egyptian Museum in Cairo, Egypt) :</strong> During on-site visits, we captured <strong>230 high-resolution images</strong> of various artifacts, including Pharaonic coffins, statues, and wall inscriptions housed in the Egyptian Museum in Cairo.</li> <li><strong>Online Museum Repositories:</strong> We leveraged the growing trend of museums providing open access to their collections, gathering over <strong>300 images</strong> from <strong>The Metropolitan Museum of Art (“The Met”), New York, USA </strong>. The Met’s extensive collection spans approximately 30,000 objects of artistic, historical, and cultural significance, dating from around 300,000 BCE to the 4th century CE.</li> <li><strong>Museo Egizio, Turin, Italy:</strong> We also collected more than <strong>200 images</strong> from the <strong>Egyptian Museum in Turin (“Museo Egizio”)</strong>, which has made over 4,000 of its approximately 40,000 objects accessible online.</li> <li><strong>Specific Archaeological Sites:</strong> The dataset includes <strong>18 images</strong> of the wall inscriptions within the <strong>Unas pyramid in Giza, Egypt</strong>, known for their dense concentration of hieroglyphic signs.</li> <li><strong>Additional Online Resources:</strong> Further images were sourced from the online collections of: <ul> <li>The French Institute for Oriental Archaeology (IFAO) in Cairo</li> <li>The Rosicrucian Egyptian Museum (REM), USA</li> <li>The British Museum, UK</li> <li>The Museum of Fine Arts in Boston, USA</li> <li>The Louvre Museum in Paris</li> <li>The Egyptian Museum and Papyrus Collection of Berlin, Germany</li> <li>General online searches for relevant artifacts.</li> </ul> </li> </ul> <p></p> <p><strong>Annotation Details:</strong> Following image collection and rigorous selection, every instance within the HLA Dataset underwent manual segmentation and annotation using the CVAT platform . Polygonal segmentation masks were created for two primary classes: <strong>“Line”</strong> (representing rows of hieroglyphs) and <strong>“Cartouche”</strong> (oval enclosures containing royal names). Individual images within the dataset can contain a significant number of these objects, with some images featuring up to <strong>152 distinct lines or cartouches</strong>.</p> <p><strong>Key Statistics:</strong></p> <ul> <li><strong>Total Images:</strong> 897</li> <li><strong>Annotation Classes:</strong> 2 (“Line”, “Cartouche”)</li> <li><strong>Annotation Type:</strong> Polygon Segmentation Masks</li> <li><strong>Maximum Objects per Image:</strong> 152</li> </ul> <p><strong>Potential Uses:</strong> This dataset is ideal for training and evaluating models for:</p> <ul> <li>Hieroglyphic line detection and segmentation.</li> <li>Cartouche detection and segmentation.</li> <li>Layout analysis of hieroglyphic inscriptions on various artifact types.</li> </ul> <p><strong>Json annotation files "in coco format":</strong> </p> <ul> <li>Train: 705 images.</li> <li>Validation: 178 images.</li> <li>Test: 10 images.</li> </ul> </section> </section> </body> </html>
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Harvard Dataverse
创建时间:
2025-04-07
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