  Historical maps contain detailed geographic information difficult to find
elsewhere covering long-periods of time (e.g., 125 years for the historical
topographic maps in the US). However, these maps typically exist as scanned
images without searchable metadata. Existing approaches making historical maps
searchable rely on tedious manual work (including crowd-sourcing) to generate
the metadata (e.g., geolocations and keywords). Optical character recognition
(OCR) software could alleviate the required manual work, but the recognition
results are individual words instead of location phrases (e.g., "Black" and
"Mountain" vs. "Black Mountain"). This paper presents an end-to-end approach to
address the real-world problem of finding and indexing historical map images.
This approach automatically processes historical map images to extract their
text content and generates a set of metadata that is linked to large external
geospatial knowledge bases. The linked metadata in the RDF (Resource
Description Framework) format support complex queries for finding and indexing
historical maps, such as retrieving all historical maps covering mountain peaks
higher than 1,000 meters in California. We have implemented the approach in a
system called mapKurator. We have evaluated mapKurator using historical maps
from several sources with various map styles, scales, and coverage. Our results
show significant improvement over the state-of-the-art methods. The code has
been made publicly available as modules of the Kartta Labs project at
https://github.com/kartta-labs/Project.
