ICDAR 2021 Competition on Historical Map Segmentation

From LRDE

Abstract

This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg)encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task 2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task 3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks 2 and 3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at https://icdar21-mapseg.github.io/.

Documents

Bibtex (lrde.bib)

@InProceedings{	  chazalon.21.icdar.2,
  title		= {ICDAR 2021 Competition on Historical Map Segmentation},
  author	= {Joseph Chazalon and Edwin Carlinet and Yizi Chen and
		  Julien Perret and Bertrand Dum\'enieu and Cl\'ement Mallet
		  and Thierry G\'eraud},
  booktitle	= {Proceedings of the 16th International Conference on
		  Document Analysis and Recognition (ICDAR'21)},
  year		= {2021},
  month		= sep,
  pages		= {},
  address	= {Lausanne, Switzerland},
  abstract	= {This paper presents the final results of the ICDAR 2021
		  Competition on Historical Map Segmentation (MapSeg),
		  encouraging research on a series of historical atlases of
		  Paris, France, drawn at 1/5000 scale between 1894 and 1937.
		  The competition featured three tasks, awarded separately.
		  Task~1 consists in detecting building blocks and was won by
		  the L3IRIS team using a DenseNet-121 network trained in a
		  weakly supervised fashion. This task is evaluated on 3
		  large images containing hundreds of shapes to detect.
		  Task~2 consists in segmenting map content from the larger
		  map sheet, and was won by the UWB team using a U-Net-like
		  FCN combined with a binarization method to increase
		  detection edge accuracy. Task~3 consists in locating
		  intersection points of geo-referencing lines, and was also
		  won by the UWB team who used a dedicated pipeline combining
		  binarization, line detection with Hough transform,
		  candidate filtering, and template matching for intersection
		  refinement. Tasks~2 and~3 are evaluated on 95 map sheets
		  with complex content. Dataset, evaluation tools and results
		  are available under permissive licensing at
		  \url{https://icdar21-mapseg.github.io/}.},
  note		= {To appear}
}