Abstract
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 active-learning 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.
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Notes
- 1.
Personal communication with Dr. H.-G. Schmidt, head of the Manuscripts and Early Prints department, Würzburg University Library.
- 2.
Würzburg University Library, http://www.franconica-online.de/.
- 3.
Note that this basic approach is not invariant to scale and rotation. It is naturally robust against small variations, but some historical maps would require a more advanced template matching algorithm.
- 4.
See [17] and http://scikit-learn.org/.
- 5.
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Acknowledgments
We thank Wouter Duivesteijn for fruitful discussion and helpful comments. We thank Hans-Günter Schmidt of the Würzburg University Library for providing real data and practical use cases.
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Budig, B., van Dijk, T.C. (2015). Active Learning for Classifying Template Matches in Historical Maps. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_5
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