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/.
This work was partially funded by the French National Research Agency (ANR): Project SoDuCo, grant ANR-18-CE38-0013. We thank the City of Paris for granting us with the permission to use and reproduce the atlases used in this work.
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Notes
- 1.
Atlas municipal des vingt arrondissements de Paris. 1894, 1895, 1898, 1905, 1909, 1912, 1925, 1929, and 1937. Bibliothèque de l’Hôtel de Ville. City of Paris. France. Online resources for the 1925 atlas: https://bibliotheques-specialisees.paris.fr/ark:/73873/pf0000935524.
References
Baloun, J., Král, P., Lenc, L.: ChronSeg: novel dataset for segmentation of handwritten historical chronicles. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART), pp. 314–322 (2021)
Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using Augmentor. Bioinformatics 35(21), 4522–4524 (2019)
Blusseau, S., Velasco-Forero, S., Angulo, J., Bloch, I.: Tropical and morphological operators for signal processing on graphs. In: Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), pp. 1198–1202 (2018)
Chazalon, J., Carlinet, E.: Revisiting the coco panoptic metric to enable visual and qualitative analysis of historical map instance segmentation. In: 16th International Conference on Document Analysis and Recognition (ICDAR) (2021, to appear)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Proceedings of the 17th International Conference on Neural Information Processing Systems (NIPS), pp. 529–536 (2004)
Hernández, J., Marcotegui, B.: Morphological segmentation of building façade images. In: Proceedings of the 16th International Conference on Image Processing (ICIP), pp. 4029–4032. IEEE (2009)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017)
Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: Panoptic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9404–9413 (2019)
Lemaitre, A., Camillerapp, J., Coüasnon, B.: Multiresolution cooperation makes easier document structure recognition. Int. J. Doc. Anal. Recognit. (IJDAR) 11(2), 97–109 (2008)
Leplumey, I., Camillerapp, J., Queguiner, C.: Kalman filter contributions towards document segmentation. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 765–769 (1995)
Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. arXiv preprint arXiv:2104.01538 (2021)
Nguyen, N., Rigaud, C., Revel, A., Burie, J.: A learning approach with incomplete pixel-level labels for deep neural networks. Neural Netw. 130, 111–125 (2020)
Nina, O., Morse, B., Barrett, W.: A recursive OTSU thresholding method for scanned document binarization. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 307–314. IEEE (2011)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
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Chazalon, J. et al. (2021). ICDAR 2021 Competition on Historical Map Segmentation. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_46
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