Abstract

Historical maps contain detailed geographic information difficult to find elsewhere covering long-periods of time (e.g., 125 years for the historical topographic maps in the US). However, these maps typically exist as scanned images without searchable metadata. Existing approaches making historical maps searchable rely on tedious manual work (including crowd-sourcing) to generate the metadata (e.g., geolocations and keywords). Optical character recognition (OCR) software could alleviate the required manual work, but the recognition results are individual words instead of location phrases (e.g., "Black'' and "Mountain'' vs. "Black Mountain''). This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images. This approach automatically processes historical map images to extract their text content and generates a set of metadata that is linked to large …

Date

August 23, 2020

Authors

Zekun Li, Yao-Yi Chiang, Sasan Tavakkol, Basel Shbita, Johannes H Uhl, Stefan Leyk, Craig A Knoblock

Book

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages

3290-3298