The Generalized Intersection over Union (GIoU) and the Manhattan distance between axis-aligned boxes represented either as corner coordinates or their center and size, are extended to accept a range of bounding boxes as ground truth, producing the metrics RIoU, $R_1$ and $R^t_1$, respectively. In the context of Table Detection it is shown that this box relaxation procedure allows training object detection models with partial or inexact annotations. For the Table Structure Recognition task, several code improvements to Microsoft's open-source Table Transformer increase all $\mathrm{GriTS}$ metrics on PubTables-1M, with the overall accuracy increasing from 0.8326 to 0.8433. Then box relaxation... More >