Automatic Georeferencing of Map Images Using Unsupervised Learning and Graph Analysis

"We present a novel method for the automatic georeferencing of heterogeneous map images based on the analysis of the spatial relationships between their lines of text and the geographical locations they depict. Our approach differs from previous work in that the only input provided is the raster image, it does not require additional hint or metadata. The method is also designed to be highly tolerant to maps with different art styles, scales, orientations and cartographic projections. To accomplish this task we leverage the power of modern OCR (Optical Character Recognition) and geocoding services to generate a series of candidate ground control points (GCP) and then discriminate between them using a combination of clustering algorithms and graph analysis. Experimental results for 359 map images demonstrate the viability proposed method. We achieved a precision ranging from 80% to 97% and a recall higher than 60%."