Recent advances in writer identification push the limits by using increasingly complex methods relying on sophisticated preprocessing, or the combination of already complex descriptors. In this paper, we pursue a simpler and faster approach to writer identification, introducing novel descriptors computed from the geometrical arrangement of interest points at different scales. They capture orientation distributions and geometrical relationships of script parts such as strokes, junctions, endings, and loops. Thus, we avoid a fixed set of character appearances as in standard codebook-based methods. The proposed descriptors significantly cut down processing time compared to existing methods, are simple and efficient, and can be applied out-of-the-box to an unseen dataset. Evaluations on widely-used datasets show their potential when applied by themselves, and in combination with other descriptors. Limitations of our method relate to the amount of data needed to obtain reliable models.