# Text region detection in historical astronomical diagrams

arXiv: https://arxiv.org/abs/2606.15886 · HTML: https://mlsanity.com/paper/2606.15886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 26 of 26)

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- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network](https://mlsanity.com/paper/1908.05900/) (2019)
- [Text Spotting Transformers](https://mlsanity.com/paper/2204.01918/) (2022)
- [TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images](https://mlsanity.com/paper/2001.01469/) (2020)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [A Two-Stage Method for Text Line Detection in Historical Documents](https://mlsanity.com/paper/1802.03345/) (2018)
- [Omnidirectional Scene Text Detection with Sequential-free Box Discretization](https://mlsanity.com/paper/1906.02371/) (2019)
- [Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization](https://mlsanity.com/paper/2007.06890/) (2020)
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- [ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records](https://mlsanity.com/paper/1903.03341/) (2019)
- [Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection](https://mlsanity.com/paper/2112.06104/) (2021)
- [The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps](https://mlsanity.com/paper/2306.17059/) (2023)
- [DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting](https://mlsanity.com/paper/2305.19957/) (2023)
- [Hyper-Local Deformable Transformers for Text Spotting on Historical Maps](https://mlsanity.com/paper/2506.15010/) (2025)
- [Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods](https://mlsanity.com/paper/2203.12346/) (2022)
- [LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting](https://mlsanity.com/paper/2511.05818/) (2025)
- [Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts](https://mlsanity.com/paper/2108.09436/) (2021)
- [Historical Astronomical Diagrams Decomposition in Geometric Primitives](https://mlsanity.com/paper/2403.08721/) (2024)
- [SegHist: A General Segmentation-based Framework for Chinese Historical Document Text Line Detection](https://mlsanity.com/paper/2406.15485/) (2024)
- [Baseline Detection in Historical Documents using Convolutional U-Nets](https://mlsanity.com/paper/1810.09343/) (2018)

## Abstract (arXiv)

Text detection is a crucial task in the analysis of historical documents. While datasets and benchmarks exist for text detection in manuscripts and maps, the study of text in mathematical diagrams has received little attention. To address this, we introduce a large-scale, diverse, open-access dataset of 948 historical astronomical diagrams containing 10,940 oriented polygonal text regions. Our dataset spans ten centuries (8th to 18th) and seven main linguistic traditions: Arabic and Persian (115), Chinese (332), Byzantine (233), Latin (185), Hebrew (48), and Sanskrit (35). It captures a wide range of diagram styles and textual content, from symbols to multi-line paragraphs. Each text instance is annotated with ordered polygons that precisely delineate text regions and encode the reading direction. In addition, we annotated the 2,293 regions in Latin diagrams with 20 class labels. We evaluated several strong baselines on our dataset, including TESTR, DeepSolo++, and Poly-DETR, a simple extension of DINO-DETR that we design to predict ordered polygon vertices. Poly-DETR achieves state-of-the-art performance on the MTHv2 and cBAD2019 benchmarks and provides a solid, simple baseline on our dataset. Code and dataset available online.