Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation

• URL: http://arxiv.org/abs/2101.02144v1
• Date: Wed, 6 Jan 2021 17:24:57 GMT
• Title: Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
• Authors: Yizi Chen (1,2), Edwin Carlinet (1), Joseph Chazalon (1), Cl\'ement Mallet (2), Bertrand Dum\'enieu (3), Julien Perret (2,3) ((1) EPITA Research and Development Lab. (LRDE), EPITA, France, (2) Univ. Gustave Eiffel, IGN-ENSG, LaSTIG, (3) LaD\'eHiS, CRH, EHESS)
• Abstract summary: 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 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.
• Score: 22.050293193182238
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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