The Early Stage in the Methodology of Automatic Toponym Recognition on Geographic Maps and in Texts
DOI:
https://doi.org/10.52575/2712-7443-2025-49-1-128-145Keywords:
historical maps, toponyms, automatic recognition, computer vision, machine learningAbstract
The article examines the evolution of methodological approaches to automatic toponym recognition in cartographic materials and texts from the late 1980s to the mid-2010s. It analyzes key research works of the 2000s that laid the foundation for modern computer vision and machine learning methods in this field. Special attention is paid to the challenges of toponym recognition on historical maps, including issues of homonymy, temporal ambiguity, and multilingualism. The study investigates mathematical methods and algorithmic solutions from various research groups, evaluating their effectiveness and limitations in handling complex cartographic materials. The works by Smith and Crane, Gelbukh and Levachkine, Pouderoux, and other scientists who made significant contributions to methodology development are analyzed in detail. Particular emphasis is placed on the three-phase approach to semi-automatic toponym recognition and the specific challenges encountered when processing historical maps. The research demonstrates methodological continuity in the development of this direction and highlights the enduring relevance of classical approaches in the context of modern artificial intelligence technologies, suggesting ways to integrate traditional methods with contemporary neural network architectures.
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Lenc L., Martínek J., Baloun J., Prantl M., Král P. 2022. Historical Map Toponym Extraction for Efficient Information Retrieval. In: Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science. Springer: 171–183. https://doi.org/10.1007/978-3-031-06555-2_12
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Levachkine S., Vel´azquez A., Alexandrov V., Kharinov M. 2002. Semantic Analysis and Recognition of Raster-Scanned Color Cartographic Images. In: Graphics Recognition Algorithms and Applications. GREC 2001. Lecture Notes in Computer Science. Berlin, Heidelberg, Springer-Verlag: 178–189.
Milleville K., Verstockt S., Van de Weghe N. 2020. Improving Toponym Recognition Accuracy of Historical Topographic Maps. Automatic Vectorisation of Historical Maps, Proceedings of the International Workshop on Automatic Vectorisation of Historical Maps, Budapest, Hungary, 13: 63–72.
Olson R., Kim J., Chiang Y.Y. 2024. Automatic Search of Multiword Place Names on Historical Maps. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, 9–12. https://doi.org/10.1145/3681769.3698577
Peters M., Neumann M., Iyyer M., Gardner M., Clark Chr., Lee K, Zettlemoyer L. 2018. Deep Contextualized Word Representations. In: Human Language Technologies. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, New Orleans, Association for Computational Linguistics, Vol. 1: 2227–2237.
Pierrot-Deseilligny M., Men H.L., Stamon G. 1995. Characters String Recognition on Maps, a Method for High Level Reconstruction. Montreal, QC, Canada, Proceedings of ICDAR: 249–252.
Pouderoux J., Gonzato J.-C., Pereira A., Guitton P. 2007. Toponym Recognition in Scanned Color Topographic Maps. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 1: 531–535.
Schlegel I. 2021. Automated Extraction of Labels from Large-Scale Historical Maps. AGILE: GIScience Series, 2: 12. https://doi.org/10.5194/agile-giss-2-12-2021
Simon R., Pilgerstorfer P., Isaksen L., Barker E. 2014. Towards Semi-Automatic Annotation of Toponyms on old maps. e-Perimetron, 9(3): 105–128.
Smith D.A., Crane G.R. 2001. Disambiguating Geographic Names in A Historical Digital Library. In: Research and Advanced Technology for Digital Libraries. ECDL 2001. Lecture Notes in Computer Science, Springer, Berlin: 127–136.
Vel´azquez A., Levachkine S. 2003. Text/graphics separation and recognition in raster-scanned color cartographic maps. Proceedings: Automated geographic indexing of text documents. Journal of the American Society for Information Science, 45(9): 645–655.
Zhou B., Zou L., Hu Y., Qiang Y., Goldberg D. 2023. TopoBERT: a Plug and Play Toponym Recognition Module Harnessing Fine-Tuned BERT. International Journal of Digital Earth, 16(1): 3045–3064.
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