Abstract
Abstract. 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.
Highlights
Extracting labels from historical maps is not as straightforward as it is the case for current maps (Chiang, 2017; Lin and Chiang, 2018)
A frequent lack of in-depth information, which is generally implemented by databases within current maps, impairs a simple search or analysis of places, street or building names, and other local designations within historical maps
Various challenges arose when working with Strabo. Due to their frequently similar visual characteristics, the algorithm does not differ between text and similar graphical elements such as textures or edges of map objects, between those being of the same color
Summary
Extracting labels from historical maps is not as straightforward as it is the case for current maps (Chiang, 2017; Lin and Chiang, 2018). The purpose of this study is to demonstrate a universal solution for an automated detection and recognition of text elements from large-scale (≥1:10,000, Kohlstock (2004)) historical maps without the need of making major individual adjustments for individual maps. With this goal in mind, we have been able to locate and label geographical features which, in general, are not accessible from historical maps. A contribution to an approximate georeferencing of historical maps has been made
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