Active Learning for Classifying Template Matches in Historical Maps [chapter]

Benedikt Budig, Thomas C. van Dijk
2015 Lecture Notes in Computer Science  
Historical maps are important sources of information for scholars of various disciplines. Many libraries are digitising their map collections as bitmap images, but for these collections to be most useful, there is a need for searchable metadata. Due to the heterogeneity of the images, metadata are mostly extracted by hand-if at all: many collections are so large that anything more than the most rudimentary metadata would require an infeasible amount of manual effort. We propose an
more » ... g approach to one of the practical problems in automatic metadata extraction from historical maps: locating occurrences of image elements such as text or place markers. For that, we combine template matching (to locate possible occurrences) with active learning (to efficiently determine a classification). Using this approach, we design a human computer interaction in which large numbers of elements on a map can be located reliably using little user effort. We experimentally demonstrate the effectiveness of this approach on real-world data.
doi:10.1007/978-3-319-24282-8_5 fatcat:ajcbwfrt5jgqvjtyt5zccjzdni