# The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps

arXiv: https://arxiv.org/abs/2306.17059 · HTML: https://mlsanity.com/paper/2306.17059/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition](https://mlsanity.com/paper/2203.10209/) (2022)
- [Text Spotting Transformers](https://mlsanity.com/paper/2204.01918/) (2022)
- [Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection](https://mlsanity.com/paper/2112.06104/) (2021)
- [An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images](https://mlsanity.com/paper/2112.01671/) (2021)

## Built on by (most cited first, 8 of 8)

- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [Hyper-Local Deformable Transformers for Text Spotting on Historical Maps](https://mlsanity.com/paper/2506.15010/) (2025)
- [DIGMAPPER: A Modular System for Automated Geologic Map Digitization](https://mlsanity.com/paper/2506.16006/) (2025)
- [Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning](https://mlsanity.com/paper/2510.08385/) (2025)
- [LIGHT: Multi-Modal Text Linking on Historical Maps](https://mlsanity.com/paper/2506.22589/) (2025)
- [Generalizable Multiscale Segmentation of Heterogeneous Map Collections](https://mlsanity.com/paper/2603.05037/) (2026)
- [Text region detection in historical astronomical diagrams](https://mlsanity.com/paper/2606.15886/) (2026)
- [Automated Detection and Encoding of Cartographic Signs in Historical Maps](https://mlsanity.com/paper/2610.13224/) (2026)

## Abstract (arXiv)

Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools like the Google Vision APIs for automatically transcribing text from these maps into machine-readable formats, they do not work well with large-sized images (e.g., high-resolution scanned documents), cannot infer the relation between the recognized text and other datasets, and are challenging to integrate with post-processing tools. This paper introduces the mapKurator system, an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans. The output data, comprising bounding polygons and recognized text, is in the standard GeoJSON format, making it easily modifiable within Geographic Information Systems (GIS). The proposed system allows users to quickly generate valuable data from large numbers of historical maps for in-depth analysis of the map content and, in turn, encourages map findability, accessibility, interoperability, and reusability (FAIR principles). We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection. We also demonstrated a seamless integration of mapKurator with a collaborative web platform to enable accessing automated approaches for extracting and linking text labels from historical map scans and collective work to improve the results.