Families of similar elements are detected using a pipeline comprising computer vision algorithms for square detection in images (CV), and a custom statistical noise reduction algorithm (SNR). This pipeline exploits the structure of re-shuffled similarity matrices and leads to reproducible and parameter-independent results. The pipeline takes a similarity matrix and an ensemble of optimised sequences as input, and outputs a collection of families of elements. In the first step, 15 images are produced from reshuffling the similarity matrices, each using one of the sequences of the ensemble. For each image, CV is applied with N different sets of parameters, each yielding a collection of families. SNR is then applied over the results of each image, reducing the results from the previous step to a single collection of families per image. SNR is applied once again over these resulting collections, producing a single collection of families which is the output of the pipeline. Further information is found in SI Fig. .