  The digitization of historical maps enables the study of ancient, fragile,
unique, and hardly accessible information sources. Main map features can be
retrieved and tracked through the time for subsequent thematic analysis. The
goal of this work is the vectorization step, i.e., the extraction of vector
shapes of the objects of interest from raster images of maps. We are
particularly interested in closed shape detection such as buildings, building
blocks, gardens, rivers, etc. in order to monitor their temporal evolution.
Historical map images present significant pattern recognition challenges. The
extraction of closed shapes by using traditional Mathematical Morphology (MM)
is highly challenging due to the overlapping of multiple map features and
texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are
perfectly designed for content image filtering but provide no guarantee about
closed shape detection. Also, the lack of textural and color information of
historical maps makes it hard for CNN to detect shapes that are represented by
only their boundaries. Our contribution is a pipeline that combines the
strengths of CNN (efficient edge detection and filtering) and MM (guaranteed
extraction of closed shapes) in order to achieve such a task. The evaluation of
our approach on a public dataset shows its effectiveness for extracting the
closed boundaries of objects in historical maps.
