Intelligent Map Reader: A Framework for Topographic Map Understanding With Deep Learning and Gazetteer

Abstract

Text features in topographic maps are important for helping users to locate the area that a map covers and to understand the map’s content. Previous works on the optical detection of map text from topographic maps have used geometric features, the Hough transform, and segmentation. However, these approaches still face challenges when detecting map text in complicated contexts, especially when the map text is touching other map features, such as contours or geographical features. Thus, state-of-the-art techniques for map text and feature recognition and manual interpretation and correction are always required to produce accurate results when optically converting topographic maps into a readable format. This paper proposes a methodological framework called the intelligent map reader that enables the automatic and accurate optical understanding of the content of a topographic map using deep learning techniques in combination with a gazetteer. The intelligent map reader framework includes the detection of map text via deep learning, the separation of text units via graph-based segmentation and clustering, optical character recognition (OCR) via an OCR engine, and digital-gazetteer-based map content understanding. Experimental results validate the efficiency and robustness of our proposed methodology for map text recognition and map content understanding. We expect the proposed intelligent map reader to contribute to various applications in the GeoAI field.