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

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