This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, non-rigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.