# Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection

arXiv: https://arxiv.org/abs/2112.06104 · HTML: https://mlsanity.com/paper/2112.06104/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019](https://mlsanity.com/paper/1907.00945/) (2019)
- [TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes](https://mlsanity.com/paper/1807.01544/) (2018)
- [ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)](https://mlsanity.com/paper/1708.09585/) (2017)
- [R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection](https://mlsanity.com/paper/1706.09579/) (2017)
- [UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World](https://mlsanity.com/paper/2003.10608/) (2020)
- [An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images](https://mlsanity.com/paper/2112.01671/) (2021)
- [Chinese Text in the Wild](https://mlsanity.com/paper/1803.00085/) (2018)

## Built on by (most cited first, 10 of 10)

- [The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps](https://mlsanity.com/paper/2306.17059/) (2023)
- [Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps](https://mlsanity.com/paper/2310.12616/) (2023)
- [Hyper-Local Deformable Transformers for Text Spotting on Historical Maps](https://mlsanity.com/paper/2506.15010/) (2025)
- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [Segmenting France Across Four Centuries](https://mlsanity.com/paper/2505.24824/) (2025)
- [GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research](https://mlsanity.com/paper/2210.13207/) (2022)
- [Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation](https://mlsanity.com/paper/2511.15875/) (2025)
- [Deep learning enables urban change profiling through alignment of historical maps](https://mlsanity.com/paper/2602.02154/) (2026)
- [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)

## Abstract (arXiv)

Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-ofdomain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open- StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations easily. However, the cartographic styles between OSM map tiles and historical maps are significantly different. This paper proposes a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We use a style transfer model to convert contemporary map images into historical style and place text labels upon them. We show that the state-of-the-art text detection models (e.g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.