A general method for detection, rectification and segmentation of co-planar repeated patterns imaged will presented. The only assumption on the image content is that repeated elements of the pattern can be mapped to each other in the scene plane by a set of Euclidean transformations. This is a very general assumption that covers nearly all commonly seen man-made repetitive patterns. In addition, novel linear constraints are exploited that enable geometric ambiguity reduction between the rectification of the imaged pattern and the real-world pattern. The remaining ambiguity is within a similarity if the scene plane contains repeated elements that are rotated differently, or within a similarity with a scale ambiguity along the axis of symmetry if any of the elements are reflected. The method is successfully tested on a broad range of image types including those where state-of-the-art methods fail.