In the context of digital watermarking of images/video, template based techniques rely on the insertion of a signal template to aid recovery of the watermark after transforms (rotation, scale, translation, aspect-ratio) common in imaging workflows. Detection approaches for such techniques often rely on known signal properties when performing geometry estimation before watermark extraction. In deep watermarking, i.e., watermarking employing deep learning, focus so far has been on extraction methods that are invariant to geometric transforms. This results in a gap in precise geometry recovery and synchronization which compromises watermark recovery, including the recovery of information bits, i.e., the payload. In this work, we propose DeepSync, a novel deep learning approach aimed at enhancing watermark synchronization for both template-based and deep watermarks.