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Conference Papers Year : 2021

Segmentation of historical maps without annotated data

Jean Camillerapp
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  • PersonId : 932492

Abstract

This paper presents the method which we submitted to the competition of Historical Map Segmentation, in ICDAR'21. The goal is to segment document images of Paris maps from the beginning of the 20th century: delineate the content of the map and locate the graticule line intersections, which give indications on the geographic coordinates of the map. Our contribution is entirely led by a rule-based method. Thus, it does not require a training phase, nor annotated data. We extract the line segments, in the image, at various resolutions. Then, a grammatical description combines those elements in a logical way to filter the relevant map contents. This is an original approach, at the era of deep learning techniques, but that is very convenient for old non-annotated documents. We validated this work during MapSeg competition. It obtained the third position for map segmentation and the second position for graduate line detection, with a localization score of 89.2%.
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Dates and versions

hal-03374571 , version 1 (12-10-2021)

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Aurélie Lemaitre, Jean Camillerapp. Segmentation of historical maps without annotated data. 6th International Workshop on Historical Document Imaging and Processing (HIP’21), Sep 2021, Lausanne, France. pp.19-24, ⟨10.1145/3476887.3476909⟩. ⟨hal-03374571⟩
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