Abstract
Text on digitized historical maps contains valuable information, e.g., providing georeferenced political and cultural context. The goal of the ICDAR 2024 MapText Competition is to benchmark methods that automatically extract textual content on historical maps (e.g., place names) and connect words to form location phrases. The competition features two primary tasks—text detection and end-to-end text recognition—each with a secondary task of linking words into phrase blocks. Submissions are evaluated on two data sets: 1) David Rumsey Historical Map Collection which contains 936 map images covering 80 regions and 183 distinct publication years (from 1623 to 2012); 2) French Land Registers (created during the 19th century) which contains 145 map images of 50 French cities and towns. The competition received 44 submissions among all tasks. This report presents the motivation for the competition, the tasks, the evaluation metrics, and the submission analysis.
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Acknowledgements
The authors thank David Rumsey for his generous support for the competition. We thank Sergi Robles and Dimosthenis Karatzas for support with the RRC server. This work is partially supported by the French Ministry of the Armed Forces - Defence Innovation Agency (AID). Digitized French land registers are provided by the Archives of the French department of Val-de-Marne (AD94).
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Li, Z. et al. (2024). ICDAR 2024 Competition on Historical Map Text Detection, Recognition, and Linking. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14809. Springer, Cham. https://doi.org/10.1007/978-3-031-70552-6_22
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