Abstract
Recognizing text in historical maps is inherently difficult due to input challenges such as artifacts interfering with the text or an unpredictable rotation and orientation of the text. This paper discusses our algorithm that overcomes the limitations of the input by adding extra input consisting of multiple layers of images of the same map area but across different time periods and names of geographic entities in the United Kingdom collected from OpenStreetMap. Using our algorithm, compared to Strabo, a state-of-the-art text recognition software on maps, we obtain a 153% improvement in precision, a 31% improvement in recall, and a 75% improvement in F-score for word recognition on maps.
| Original language | English (US) |
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| Title of host publication | 2016 23rd International Conference on Pattern Recognition, ICPR 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3993-3998 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509048472 |
| DOIs | |
| State | Published - Jan 1 2016 |
| Externally published | Yes |
| Event | 23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: Dec 4 2016 → Dec 8 2016 |
Publication series
| Name | Proceedings - International Conference on Pattern Recognition |
|---|---|
| Volume | 0 |
| ISSN (Print) | 1051-4651 |
Other
| Other | 23rd International Conference on Pattern Recognition, ICPR 2016 |
|---|---|
| Country/Territory | Mexico |
| City | Cancun |
| Period | 12/4/16 → 12/8/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.