Automatically extracting the geometric content from the hundreds of thousands
of diagrams drawn in historical manuscripts would enable historians to study
the diffusion of astronomical knowledge on a global scale. However,
state-of-the-art vectorization methods, often designed to tackle modern data,
are not adapted to the complexity and diversity of historical astronomical
diagrams. Our contribution is thus twofold. First, we introduce a unique
dataset of 303 astronomical diagrams from diverse traditions, ranging from the
XIIth to the XVIIIth century, annotated with more than 3000 line segments,
circles and arcs. Second, we develop a model that builds on DINO-DETR to enable
the prediction of multiple geometric primitives. We show that it can be trained
solely on synthetic data and accurately predict primitives on our challenging
dataset. Our approach widely improves over the LETR baseline, which is
restricted to lines, by introducing a meaningful parametrization for multiple
primitives, jointly training for detection and parameter refinement, using
deformable attention and training on rich synthetic data. Our dataset and code
are available on our webpage.

从历史手稿中自动提取几十万个图表中的几何内容将使历史学家能够在全球范围内研究天文知识的传播。我们提供了一个独特的数据集，包含 303 个来自不同传统的天文图表，从 12 世纪到 18 世纪，注释了 3000 多个线段、圆和弧。我们还开发了一个模型，该模型基于 DINO-DETR，可以预测多个几何基元。我们展示了该模型可以仅通过合成数据进行训练，并准确地预测我们具有挑战性的数据集上的基元。我们的方法在多个基元的意义化参数化、检测和参数细化的联合训练以及使用丰富的合成数据进行训练方面明显改进了 LETR 基准。我们的数据集和代码可在我们的网页上获取。

历史天文图解的几何基元分解

Historical Astronomical Diagrams Decomposition in Geometric Primitives

We apply the TrOCR framework to real-world, historical manuscripts and show
that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has
been trained on English only, but it can adapt to other languages that use the
Latin alphabet fairly easily and with little training material. We compare
TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such
systems. This finding is essential since Transkribus performs best when it has
access to baseline information, which is not needed at all to fine-tune TrOCR.

本文 比较了 TrOCR 框架和 SOTA HTR 框架（Transkribus） 用于 对历史手稿的识别，发现 TrOCR 表示出类似或更好的性能且少量训练即可在英语以外的其他基于拉丁字母的语种上进行迁移学习。