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Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlooks

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 709))

Abstract

Digital map processing has been an interest in the image processing and pattern recognition community since the early 80s. With the exponential growth of available map scans in the archives and on the internet, a variety of disciplines in the natural and social sciences grow interests in using historical maps as a primary source of geographical and political information in their studies. Today, many organizations such as the United States Geological Survey, David Rumsey Map Collection, OldMapsOnline.org, and National Library of Scotland, store numerous historical maps in either paper or scanned format. Only a small portion of these historical maps is georeferenced, and even fewer of them have machine-readable content or comprehensive metadata. The lack of a searchable textual content including the spatial and temporal information prevents researchers from efficiently finding relevant maps for their research and using the map content in their studies. These challenges present a tremendous collaboration opportunity for the image processing and pattern recognition community to build advance map processing technologies for transforming the natural and social science studies that use historical maps. This paper presents the potentials of using historical maps in scientific research, describes the current trends and challenges in extracting and recognizing text content from historical maps, and discusses the future outlook.

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Notes

  1. 1.

    USGS NGMDB (2016) [Website]. Retrieved from http://ngmdb.usgs.gov/ngmdb/ngmdb_home.html.

  2. 2.

    USGS topoView (2016) [Website]. Retrieved from http://ngmdb.usgs.gov/maps/TopoView/.

  3. 3.

    David Rumsey. (2016). [Website]. Retrieved from http://www.davidrumsey.com/.

  4. 4.

    OldMapsOnline (2016) [Website]. Retrieved from http://www.oldmapsonline.org/.

  5. 5.

    NLS (2016) [Website]. Retrieved from http://maps.nls.uk/.

  6. 6.

    USGS topoView (2016) [Website]. Retrieved from http://ngmdb.usgs.gov/maps/TopoView/.

  7. 7.

    CalFlora (2016) [Data set]. Retrieved from http://www.calflora.org/.

  8. 8.

    CLAVIN (2016) [Computer software]. Retrieved from https://clavin.bericotechnologies.com/.

  9. 9.

    U.S. Census Gazetteer (2016) [Data set]. Retrieved from https://www.census.gov/geo/maps-data/data/gazetteer.html.

  10. 10.

    USGS GNIS (2016) [Data set]. Retrieved from http://geonames.usgs.gov/.

  11. 11.

    GeoNames (2016) [Data set]. Retrieved from http://www.geonames.org/.

  12. 12.

    OpenStreetMap (2016) [Website]. Retrieved from https://www.openstreetmap.org/.

  13. 13.

    Los Angeles Public Library Map Collection (2016) [Website]. Retrieved from https://www.lapl.org/collections-resources/visual-collections/map-collection.

  14. 14.

    NHGIS (2016) [Website]. Retrieved from https://www.nhgis.org/.

  15. 15.

    A Vision of Britain through Time (2016) [Website]. Retrieved from http://www.visionofbritain.org.uk/.

  16. 16.

    Dr. Kurashige’s article published in the Southern California Quarterly won the 2015 Carl I. Wheat Award for the best demonstration of scholarship in that journal from 2012–2014 by a senior historian.

  17. 17.

    http://www.oldhkphoto.com/coast/.

  18. 18.

    Spatial technology opens a window into history (2016) [News article]. Retrieved from https://news.usc.edu/91625/spatial-technology-opens-a-window-into-history/.

  19. 19.

    Peter Feigl's Journey Through Historical Maps (2016) [Website]. Retrieved from http://www.arcgis.com/apps/MapJournal/index.html?appid=6c3b4136b9304df09c9adcf86dd30dd5.

  20. 20.

    Tesseract-OCR (2016) [Computer software]. Retrieved from https://github.com/tesseract-ocr.

  21. 21.

    NYPL map-vectorizer (2016) [Computer software]. https://github.com/NYPL/map-vectorizer.

  22. 22.

    Plageois Commons (2016) [Website]. Retrieved from http://commons.pelagios.org/.

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Acknowledgements

This research is based upon work supported in part by the National Science Foundation under award number IIS-1564164 and in part by the University of Southern California under the Undergraduate Research Associates Program (URAP). The author thanks Travis Longcore for his input on the biology studies and the U.S. National Committee (USNC) to the International Cartographic Association (ICA) for providing travel funding to attend the 27th International Cartographic Conference (ICC).

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Authors and Affiliations

  1. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, 90089, USA

    Yao-Yi Chiang

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  1. Yao-Yi Chiang
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Correspondence to Yao-Yi Chiang .

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Editors and Affiliations

  1. The University of South Dakota, Vermillion, South Dakota, USA

    K.C. Santosh

  2. Karnatak Arts, Science and Commerce College, Bidar, India

    Mallikarjun Hangarge

  3. Polytecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  4. University of Hyderabad, Hyderabad, India

    Atul Negi

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Chiang, YY. (2017). Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlooks. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_11

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  • DOI: https://doi.org/10.1007/978-981-10-4859-3_11

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