  A correct localisation of tables in a document is instrumental for
determining their structure and extracting their contents; therefore, table
detection is a key step in table understanding. Nowadays, the most successful
methods for table detection in document images employ deep learning algorithms;
and, particularly, a technique known as fine-tuning. In this context, such a
technique exports the knowledge acquired to detect objects in natural images to
detect tables in document images. However, there is only a vague relation
between natural and document images, and fine-tuning works better when there is
a close relation between the source and target task. In this paper, we show
that it is more beneficial to employ fine-tuning from a closer domain. To this
aim, we train different object detection algorithms (namely, Mask R-CNN,
RetinaNet, SSD and YOLO) using the TableBank dataset (a dataset of images of
academic documents designed for table detection and recognition), and fine-tune
them for several heterogeneous table detection datasets. Using this approach,
we considerably improve the accuracy of the detection models fine-tuned from
natural images (in mean a 17%, and, in the best case, up to a 60%).
