Type: Chapter (Book)
Title: Automated Extraction of Labels from Large-Scale Historical Maps
Authors: Schlegel, Inga 
Source: Proceedings of the 24th AGILE Conference on Geographic Information Science
Issue Date: 4-Jun-2021
Keywords: historical maps; text detection; text recognition; text extraction; optical character recognition; levenshtein distance; georeferencing
Historical maps are frequently neither readable, searchable nor analyzable by machines due to lacking databases or ancillary information about their content. Identifying and annotating map labels is seen as a first step towards an automated legibility of those. This article investigates a universal and transferable methodology for the work with large-scale historical maps and their comparability to others while reducing manual intervention to a minimum. We present an end-to-end approach which increases the number of true positive identified labels by combining available text detection, recognition, and similarity measuring tools with own enhancements. The comparison of recognized historical with current street names produces a satisfactory accordance which can be used to assign their point-like representatives within a final rough georeferencing. The demonstrated workflow facilitates a spatial orientation within large-scale historical maps by enabling the establishment of relating databases. Assigning the identified labels to the geometries of related map features may contribute to machine-readable and analyzable historical maps.
Subject Class (DDC): 550: Geowissenschaften
HCU-Faculty: Geodäsie und Geoinformatik 
Publisher: Copernicus
Part of Series: AGILE: GIScience Series 
Volume number: 2
Publisher DOI: 10.5194/agile-giss-2-12-2021
URN (Citation Link): urn:nbn:de:gbv:1373-repos-11359
Directlink: https://repos.hcu-hamburg.de/handle/hcu/890
Language: English
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
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