Historical map georeferencing, especially when dealing with maps that exhibit high levels of local distortion, remains a time-consuming process. This paper introduces a pipeline that automates much of this process by transferring georeferencing information from georeferenced maps (Anchor) to maps lacking georeferencing (Target). At its core, the method employs deep-learning algorithms for image registration (SuperPoint and SuperGlue) alongside tailored modules to exclude outliers and enhance match density. Specifically, RANSAC is combined with a Delaunay-based procedure to discard erroneous matches and preserve consistent spatial relationships. To address the reduction in keypoints following outlier emoval, we incorporate a patch-based local image registration, enabling multiscale matching. After a final outlier-removal step, the resulting high-quality matches are used to assign real-world coordinates to the Target map. We evaluated the pipeline on 86 georeferenced historical maps of Jerusalem and obtained a root mean square error (RMSE) below 1% of the map diagonal for 71 of them. Moreover, the final georeferencing accuracy was closely tied to the number of matching keypoints, with a threshold of 100 serving as a strong indicator of reliable results. Extending the pipeline to an additional 113 non-georeferenced maps, we found that 86 were successfully georeferenced based on this keypoint threshold.