Abstract

Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-of-domain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open-StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations …

Date

November 2, 2021

Authors

Zekun Li, Runyu Guan, Qianmu Yu, Yao-Yi Chiang, Craig A Knoblock

Book

Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

Pages

17-26