Abstract
Diplomatics, the analysis of medieval charters, is a major field of research in which paleography is applied. Annotating data, if performed by laymen, needs validation and correction by experts. In this paper, we propose an effective and efficient annotation approach for charter segmentation, essentially reducing it to object detection. This approach allows for a much more efficient use of the paleographer’s time and produces results that can compete and even outperform pixel-level segmentation in some use cases. Further experiments shed light on how to design a class ontology in order to make the best use of annotators’ time and effort. Exploiting the presence of calibration cards in the image, we further annotate the data with the physical length in pixels and train regression neural networks to predict it from image patches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Software available under an open-source licence at https://github.com/anguelos/frat and in pypi https://pypi.org/project/frat/.
- 2.
The code has been forked from the original YOLOv5 repository, the specific scripts used for the experiments are in the bin folder https://github.com/anguelos/yolov5/tree/master/bin.
- 3.
The code used to run the resolution regression experiments, including links to the datasets, is available in a github repository https://github.com/anguelos/resolution_regressor.
References
Boroş, E., et al.: A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters. In: 2020 17th International conference on frontiers in handwriting recognition (ICFHR), pp. 79–84. IEEE (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Filatkina, N.: Historische formelhafte sprache. In: Historische formelhafte Sprache. de Gruyter (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015)
Jocher, G.: YOLOv5 by Ultralytics (2020). https://doi.org/10.5281/zenodo.3908559, https://github.com/ultralytics/yolov5
Jocher, G., et al.: ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements (2020). https://doi.org/10.5281/zenodo.4154370
Leipert, M., Vogeler, G., Seuret, M., Maier, A., Christlein, V.: The notary in the haystack – countering class imbalance in document processing with CNNs. In: Bai, X., Karatzas, D., Lopresti, D. (eds.) DAS 2020. LNCS, vol. 12116, pp. 246–261. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57058-3_18
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Neudecker, C., et al.: Ocr-d: an end-to-end open source ocr framework for historical printed documents. In: Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage, pp. 53–58 (2019)
Nicolaou, A., Christlein, V., Riba, E., Shi, J., Vogeler, G., Seuret, M.: Tormentor: deterministic dynamic-path, data augmentations with fractals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2707–2711 (2022)
Pletschacher, S., Antonacopoulos, A.: The page (page analysis and ground-truth elements) format framework. In: 2010 20th International Conference on Pattern Recognition, pp. 257–260. IEEE (2010)
Seuret, M., et al.: Combining ocr models for reading early modern printed books (2023)
Acknowledgements
The work presented in this paper has been supported by ERC Advanced Grant project (101019327) “From Digital to Distant Diplomatics" and by the DFG grant No. CH 2080/2-1 “Font Group Recognition for Improved OCR".
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nicolaou, A., Luger, D., Decker, F., Renet, N., Christlein, V., Vogeler, G. (2023). Efficient Annotation of Medieval Charters. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14193. Springer, Cham. https://doi.org/10.1007/978-3-031-41498-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-41498-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41497-8
Online ISBN: 978-3-031-41498-5
eBook Packages: Computer ScienceComputer Science (R0)
