Automatic Recognition of Toponyms: New Challenges for Digital Cartography
DOI:
https://doi.org/10.52575/2712-7443-2025-49-3-477-499Keywords:
toponyms, text recognition, geographic information systems, historical maps, computer visionAbstract
The article presents a comprehensive analysis of modern methods for automatic toponym recognition on geographic maps and in texts. The authors compare computational approaches including graph methods (MST), ensemble algorithms, morphological operations, and lexico-semantic methods. The paper examines challenges related to multi-word toponyms, geographical name ambiguity, and visual features of maps. Special attention is given to efficiency evaluation metrics and computational complexity of algorithms. The study systematizes methodological stages of toponym processing considering both Russian and international research. The research identifies key challenges in geographic name recognition: high information density on maps, intersection of text labels with other cartographic elements, and diversity of fonts, sizes, and text orientations. The paper examines computational characteristics and accuracy of various approaches, from traditional methods to modern deep learning architectures. The authors propose a concept of an integrated architecture for automatic toponym recognition that combines computer vision methods, natural language processing, and spatial analysis. The study demonstrates that the most promising direction is the development of adaptive systems capable of dynamically selecting the optimal set of algorithms depending on the characteristics of the processed data and the objectives of specific research.
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Sevgili Ö., Shelmanov A., Arkhipov M., Panchenko A., Biemann C. 2022. Neural Entity Linking: A Survey of Models Based on Deep Learning. Semantic Web, 13(3): 527–570. https://doi.org/10.3233/SW-222986
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Weissenbacher D., Tahsin T., Beard R., Figaro M., Rivera R., Scotch M., Gonzalez G. 2015. Knowledge- driven Geospatial Location Resolution for Phylogeographic Models of Virus Migration. Bioinformatics, 31(12): 348–356. https://doi.org/10.1093/bioinformatics/btv259
Woodruff A.G., Plaunt Chr. 1994. GIPSY: Automated Geographic Indexing of Text Documents. Journal of the American Society for Information Science, 45(9): 645–655.
Wu L., Petroni F., Josifoski M., Riedel S., Zettlemoyer L. 2020. Zero-shot Entity Linking with Dense Entity Retrieval. In: Computation and Language. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, 16–20 November 2020. Stroudsburg, Association for Computational Linguistics: 6397–6407.
Zhang Y., Chen Z., Zheng X., Chen N., Wang Y. 2021. Extracting the Location of Flooding Events in Urban Systems and Analyzing the Semantic Risk Using Social Sensing Data. Journal of Hydrology, 603(4): 127053. https://doi.org/10.1016/j.jhydrol.2021.127053
Zhou X., Yao C., Wen H., Wang Y., Zhou S., He W., Liang J. 2017. EAST: an Efficient and Accurate Scene Text Detector. In: Computer Science. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21–26 July 2017. HI, USA, Honolulu: 5551–5560.
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De Cao N., Izacard G., Riedel S., Petroni F. 2021. Autoregressive Entity Retrieval. Proceedings of the 9th International Conference on Learning Representations. Vienna, Austria, ICLR: 1–20.
Deseilligny M.P., Le Men H., Stamon G. 1995. Character String Recognition on Maps, a Rotation- invariant Recognition Method. Pattern Recognition Letters, 16(12): 1297–1310. https://doi.org/10.1016/0167-8655(95)00084-5
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Fletcher L.A., Kasturi R. 1988. A Robust Algorithm for Text String Separation from Mixed Text/graphics Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6): 910–918.
Gelbukh A., Levachkine S. 2002. Error Detection and Correction in Toponym Recognition in Cartographic Maps. IASTED International Conference Geopro-2002: 1–7.
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Gregory I., Donaldson C., Murrieta-Flores P., Rayson P. 2015. Geoparsing, GIS, and Textual Analysis: Current Developments in Spatial Humanities Research. International Journal of Humanities and Arts Computing, 9(1): 1–14. https://doi.org/10.3366/ijhac.2015.0135
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Hu X., Al-Olimat H., Kersten J., Wiegmann M., Klan F., Sun Y., Fan H. 2021. GazPNE: Annotation-free Deep Learning for Place Name Extraction from Microblogs Leveraging Gazetteer and Synthetic Data by Rules. International Journal of Geographical Information Science, 36(2): 310–337. https://doi.org/10.1080/13658816.2021.1947507
Hu X., Sun Y., Kersten J., Zhou Z., Klan F., Fan H. 2023. How Can Voting Mechanisms Improve the Robustness and Generalizability of Toponym Disambiguation? International Journal of Applied Earth Observations and Geoinformation, 117: 103191. https://doi.org/10.1016/j.jag.2023.103191
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Hu X., Zhou Z., Sun Y., Kersten J., Klan, F., Fan H., Wiegmann M. 2022b. GazPNE2: A General Place Name Extractor for Microblogs Fusing Gazetteers and Pretrained Transformer Models. IEEE Internet of Things Journal, 9(17): 16259–16271. https://doi.org/10.1109/JIOT.2022.3150967
Kent A.J. 2008. Cartographic Blandscapes and the New Noise: Finding the Good View in a Topographical Mashup. The Bulletin of the Society of Cartographers, 42(1–2): 29–37.
Kyramargiou E., Papakondylis Y., Scalora F., Dimitropoulos D. 2020. Changing the Map in Greece and Italy: Place-name Changes in the Nineteenth Century. The Historical Review/La Revue Historique, 17: 205–250. https://doi.org/10.12681/hr.27072
Lenc L., Martínek J., Baloun J., Prantl M., Král P. 2022. Historical Map Toponym Extraction for Efficient Information Retrieval. In: Document Analysis Systems. Cham: Springer International Publishing: 171–183. https://doi.org/10.1007/978-3-031-06555-2_12
Milleville K., Verstockt S., Van de Weghe N. 2020. Improving Toponym Recognition Accuracy of Historical Topographic Maps. In: Automatic Vectorisation of Historical Maps. International workshop organized by the ICA Commission on Cartographic Heritage into the Digital, Budapest, 13 March 2020. Budapest, Department of Cartography and Geoinformatics: 63–72.
Neumann L., Matas J. 2010. A Method for Text Localization and Recognition in Real-world Images. In: Computer Vision – ACCV 2010. Proceedings of the 10th Asian Conference on Computer Vision, New Zealand, 8–12 November 2010. Berlin, Heidelberg, Springer: 770–783.
Neumann L., Matas J. 2012. Real-time Scene Text Localization and Recognition. In: ACM Transactions on Multimedia Computing, Communications, and Applications. IEEE Conference on Computer Vision and Pattern Recognition, 16–21 June 2012. RI, USA, Providence: 3538–3545.
Olson R., Kim J., Chiang Y.Y. 2023. An Automatic Approach to Finding Geographic Name Changes on Historical Maps. In: ACM SIGSPATIAL GIS 2023. Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, 13–16 November 2023. Germany, Hamburg, Association for Computing Machinery: 1–2.
Olson R., Kim J., Chiang Y.Y. 2024. Automatic Search of Multiword Place Names on Historical Maps. In: GeoSearch 2024. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, Atlanta, 29 October 2024. Atlanta, Association for Computing Machinery: 9–12.
Pezeshk A., Tutwiler R.L. 2011. Automatic Feature Extraction and Text Recognition from Scanned Topographic Maps. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 5047–5063. https://doi.org/10.1109/TGRS.2011.2157697
Pierrot-Deseilligny M., Men H.L., Stamon G. 1995. Characters String Recognition on Maps, a Method for High Level Reconstruction. In: ICDAR '95. Proceedings of the Third International Conference on Document Analysis and Recognition, Montreal, 14–16 August 1995. QC, Canada, Montreal: 249–252.
Pouderoux J., Gonzato J-Ch., Pereira A., Guitton P. 2007. Toponym Recognition in Scanned Color Topographic Maps. In: ICDAR '07. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, 23–26 September 2007. Brazil, Curitiba: 531–535.
Schlegel I. 2021. Automated Extraction of Labels from Large-scale Historical Maps. AGILE GIScience Series, 2(12): 1–14.
Scott P., Bader M. K-F., Burgess T., Hardy G., Williams N. 2019. Global Biogeography and Invasion Risk of the Plant Pathogen Genus Phytophthora. Environmental Science and Policy, 101: 175–182. https://doi.org/10.1016/j.envsci.2019.08.020
Sekido I. 2019. Historical GIS Materials for South Asia Studies in the University of Tokyo. Journal of Urban and Regional Studies on Contemporary India, 5(2): 23–27.
Sevgili Ö., Shelmanov A., Arkhipov M., Panchenko A., Biemann C. 2022. Neural Entity Linking: A Survey of Models Based on Deep Learning. Semantic Web, 13(3): 527–570. https://doi.org/10.3233/SW-222986
Smith D.A., Crane G.R. 2001. Disambiguating Geographic Names in a Historical Digital Library. In: Research and Advanced Technology for Digital Libraries. International Conference on Theory and Practice of Digital Libraries, Berlin, 30 August 2001. Berlin, Heidelberg, Springer Berlin Heidelberg: 127–136.
Weissenbacher D., Tahsin T., Beard R., Figaro M., Rivera R., Scotch M., Gonzalez G. 2015. Knowledge- driven Geospatial Location Resolution for Phylogeographic Models of Virus Migration. Bioinformatics, 31(12): 348–356. https://doi.org/10.1093/bioinformatics/btv259
Woodruff A.G., Plaunt Chr. 1994. GIPSY: Automated Geographic Indexing of Text Documents. Journal of the American Society for Information Science, 45(9): 645–655.
Wu L., Petroni F., Josifoski M., Riedel S., Zettlemoyer L. 2020. Zero-shot Entity Linking with Dense Entity Retrieval. In: Computation and Language. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, 16–20 November 2020. Stroudsburg, Association for Computational Linguistics: 6397–6407.
Zhang Y., Chen Z., Zheng X., Chen N., Wang Y. 2021. Extracting the Location of Flooding Events in Urban Systems and Analyzing the Semantic Risk Using Social Sensing Data. Journal of Hydrology, 603(4): 127053. https://doi.org/10.1016/j.jhydrol.2021.127053
Zhou X., Yao C., Wen H., Wang Y., Zhou S., He W., Liang J. 2017. EAST: an Efficient and Accurate Scene Text Detector. In: Computer Science. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21–26 July 2017. HI, USA, Honolulu: 5551–5560.
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