  Table Detection (TD) is a fundamental task to enable visually rich document
understanding, which requires the model to extract information without
information loss. However, popular Intersection over Union (IoU) based
evaluation metrics and IoU-based loss functions for the detection models cannot
directly represent the degree of information loss for the prediction results.
Therefore, we propose to decouple IoU into a ground truth coverage term and a
prediction coverage term, in which the former can be used to measure the
information loss of the prediction results. Besides, considering the sparse
distribution of tables in document images, we use SparseR-CNN as the base model
and further improve the model by using Gaussian Noise Augmented Image Size
region proposals and many-to-one label assignments. Results under comprehensive
experiments show that the proposed method can consistently outperform
state-of-the-art methods with different IoU-based metrics under various
datasets and demonstrate that the proposed decoupled IoU loss can enable the
model to alleviate information loss.
