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.
|Original language||English (US)|
|Title of host publication||Recent Trends in Image Processing and Pattern Recognition - 1st International Conference, RTIP2R 2016, Revised Selected Papers|
|Editors||Mallikarjun Hangarge, Atul Negi, Vitoantonio Bevilacqua, K.C. Santosh|
|Number of pages||14|
|State||Published - 2017|
|Event||1st International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2016 - Bidar, India|
Duration: Dec 16 2016 → Dec 17 2016
|Name||Communications in Computer and Information Science|
|Conference||1st International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2016|
|Period||12/16/16 → 12/17/16|
Bibliographical noteFunding Information:
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).
© Springer Nature Singapore Pte Ltd. 2017.
- Digital map processing
- Geographic information system
- Historical maps
- Natural science
- Optical character recognition
- Social science
- Spatial humanity
- Text recognition