  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.
