Most of the table detection tasks are using existing off-
the-shelf methods for their detection algorithm. However, datasets that
are used for evaluation are not challenging enough due to the lack of
quantity and diversity. To have a better comparison between proposed
methods we introduce the NAS dataset in this paper for historical
digitized images. Tables in historic scientific documents vary widely in
their characteristics. They also appear alongside visually similar items,
such as maps, diagrams, and illustrations. We address these challenges
with a multi-phase procedure, outlined in this article, evaluated using
two datasets, ECCO and NAS. In our approach, we utilized the
Gabor filter to prepare our dataset for algorithmic detection with
Faster-RCNN. This method detects tables against all categories of visual
information. Due to the limitation in labeled data, particularly for
object detection, we developed a new method, namely, weakly supervision
bounding box extraction, to extract bounding boxes automatically
for our training set in an innovative way.