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.
Original language | English (US) |
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Title of host publication | Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Proceedings |
Editors | Elisa H. Barney Smith, Marcus Liwicki, Liangrui Peng |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 363-380 |
Number of pages | 18 |
ISBN (Print) | 9783031705519 |
DOIs | |
State | Published - 2024 |
Event | 18th International Conference on Document Analysis and Recognition, ICDAR 2024 - Athens, Greece Duration: Aug 30 2024 → Sep 4 2024 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14809 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 18th International Conference on Document Analysis and Recognition, ICDAR 2024 |
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Country/Territory | Greece |
City | Athens |
Period | 8/30/24 → 9/4/24 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keywords
- Historical maps
- Text detection
- Text recognition