Historical Astronomical Diagrams Decomposition in Geometric Primitives
Syrine Kalleli Scott Trigg Ségolène Albouy Mathieu Husson Mathieu Aubry
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.