Automated Extraction of Labels from Large-Scale Historical Maps

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.

Perks and Pitfalls of City Directories as a Micro-Geographic Data Source

“…Cambon et al, 2021 for R and Geopy contributers, 2021 for Python), their usefulness for historical city directories depends on the continuity of the urban street grid, street names, and house-numbering practices. 8 Alternative, historically more sensitive geo-referencing approaches match recognized addresses to spatial information that is extracted manually or automatically from historical maps (Cura et al, 2018;Schlegel, 2021). Next to locating directory entries in space, researchers will often want to classify them with respect to non-spatial dimensions, e.g.…”

Combining Remote Sensing Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

“…Such recent efforts include the mining of (historical) map collections by their content or associated metadata [32][33][34][35][36][37], automated georeferencing [18,[38][39][40] and alignment [41,42], text detection and recognition [43][44][45], or the extraction of thematic map content, often involving (deep) machine learning methods, focusing on specific geographic features such as forest [46], railroads [33,47], road network intersections [48,49] and road types [50], archeological content [51] and mining features [52], cadastral parcels boundaries [53,54], wetlands and other hydrographic features [55,56], linear features in general [57], land cover / land use [58], urban street networks and city blocks [34], building footprints [13,59,60] and historical human settlement patterns [61][62][63]. Other approaches use deep learning based computer vision for generic segmentation of historical maps [64,65], generative machine learning approaches for map style transfer [66,67] or attempt to mimic historical overhead imagery based on historical maps [68].…”