电子巴比伦图书馆中的自动楔形文字符号检测：大规模数据集与端到端楔形文字OCR流水线
Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline
AI总结：
提出基于可变形检测Transformer（DETR）的楔形文字符号检测模型，在最大标注数据集上实现28-37%的COCO指标提升，并应用于eBL语料库生成290万检测结果。
摘要：
学习阅读楔形文字泥板是一项极其艰巨的任务；因此，在大约50万块已发掘的泥板中，只有一小部分被亚述学家分析过。计算机视觉为解读提供了有前景的途径，但需要大规模、密集标注的数据集。为解决这一限制，使用了迄今为止最大的标注楔形文字符号数据集，并在173类和106类两种粒度下评估了基于可变形检测Transformer（DETR）的目标检测模型。所提出的系统集成了自动泥板侧面提取、启发式行分组和基于n-gram的文本相似性评估，以桥接视觉符号检测与文本结构，并在COCO风格检测指标上相比先前工作实现了28-37%的持续改进。在推理阶段，该方法应用于来自电子巴比伦图书馆（eBL）语料库的87,668块泥板碎片，产生了近290万个符号检测结果。尽管该方法在没有语言先验的情况下运行，并且对泥板损坏和布局变化敏感，但它为语料库范围的楔形文字分析提供了可扩展且可解释的基础，并支持未来与多模态和语言建模框架的集成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
