{"slug": "automated-sign-detection-across-the-electronic-babylonian-library", "title": "Automated sign detection across the Electronic Babylonian Library", "summary": "Researchers have developed a Deformable Detection Transformer (DETR)-based object detection model that achieved up to 28-37% improvement over prior work on COCO-style metrics for automated cuneiform sign detection, using the largest annotated cuneiform sign dataset to date with 173 and 106 class granularities. The system was applied to 87,668 tablet fragments from the Electronic Babylonian Library (eBL) corpus, producing nearly 2.9 million sign detections, providing a scalable foundation for corpus-wide cuneiform analysis.", "body_md": "# Computer Science > Computer Vision and Pattern Recognition\n\n[Submitted on 21 Jun 2026]\n\n# Title:Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline\n\n[View PDF](/pdf/2606.22608)\n\n[HTML (experimental)](https://arxiv.org/html/2606.22608v1)\n\nAbstract:Learning to read cuneiform tablets is an extremely demanding task; consequently, of the roughly half million excavated tablets, only a small fraction has been analysed by Assyriologists. Computer vision offers a promising avenue for decipherment but requires large, densely annotated datasets. To address this limitation, the largest annotated cuneiform sign dataset to date is used, and a Deformable Detection Transformer (DETR)-based object detection model is evaluated under two class granularities of 173 and 106 classes. The proposed system integrates automatic tablet-side extraction, heuristic line grouping, and n-gram-based textual similarity evaluation to bridge visual sign detection and textual structure, and achieves consistent improvements of up to 28-37% over prior work on COCO-style detection metrics. At inference, the method is applied to 87,668 tablet fragments from the Electronic Babylonian Library (eBL) corpus, producing nearly 2.9 million sign detections. Although the approach operates without linguistic priors and remains sensitive to tablet damage and layout variability, it provides a scalable and interpretable foundation for corpus-wide cuneiform analysis and supports future integration with multimodal and linguistic modelling frameworks.\n\n## Submission history\n\nFrom: Esteban Garces Arias [[view email](/show-email/2eed16d6/2606.22608)]\n\n**[v1]** Sun, 21 Jun 2026 17:31:05 UTC (10,689 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/automated-sign-detection-across-the-electronic-babylonian-library", "canonical_source": "https://arxiv.org/abs/2606.22608", "published_at": "2026-07-24 02:06:35+00:00", "updated_at": "2026-07-24 02:22:24.023213+00:00", "lang": "en", "topics": ["computer-vision", "artificial-intelligence", "machine-learning", "ai-research"], "entities": ["Electronic Babylonian Library", "Deformable Detection Transformer", "Assyriologists", "Esteban Garces Arias"], "alternates": {"html": "https://wpnews.pro/news/automated-sign-detection-across-the-electronic-babylonian-library", "markdown": "https://wpnews.pro/news/automated-sign-detection-across-the-electronic-babylonian-library.md", "text": "https://wpnews.pro/news/automated-sign-detection-across-the-electronic-babylonian-library.txt", "jsonld": "https://wpnews.pro/news/automated-sign-detection-across-the-electronic-babylonian-library.jsonld"}}