  Thousands of scanned historical topographic maps contain valuable information
covering long periods of time, such as how the hydrography of a region has
changed over time. Efficiently unlocking the information in these maps requires
training a geospatial objects recognition system, which needs a large amount of
annotated data. Overlapping geo-referenced external vector data with
topographic maps according to their coordinates can annotate the desired
objects' locations in the maps automatically. However, directly overlapping the
two datasets causes misaligned and false annotations because the publication
years and coordinate projection systems of topographic maps are different from
the external vector data. We propose a label correction algorithm, which
leverages the color information of maps and the prior shape information of the
external vector data to reduce misaligned and false annotations. The
experiments show that the precision of annotations from the proposed algorithm
is 10% higher than the annotations from a state-of-the-art algorithm.
Consequently, recognition results using the proposed algorithm's annotations
achieve 9% higher correctness than using the annotations from the
state-of-the-art algorithm.
