  Table Detection has become a fundamental task for visually rich document
understanding with the surging number of electronic documents. However, popular
public datasets widely used in related studies have inherent limitations,
including noisy and inconsistent samples, limited training samples, and limited
data sources. These limitations make these datasets unreliable to evaluate the
model performance and cannot reflect the actual capacity of models. Therefore,
this study revisits some open datasets with high-quality annotations,
identifies and cleans the noise, and aligns the annotation definitions of these
datasets to merge a larger dataset, termed Open-Tables. Moreover, to enrich the
data sources, we propose a new ICT-TD dataset using the PDF files of
Information and Communication Technologies (ICT) commodities, a different
domain containing unique samples that hardly appear in open datasets. To ensure
the label quality of the dataset, we annotated the dataset manually following
the guidance of a domain expert. The proposed dataset is challenging and can be
a sample of actual cases in the business context. We built strong baselines
using various state-of-the-art object detection models. Our experimental
results show that the domain differences among existing open datasets are minor
despite having different data sources. Our proposed Open-Tables and ICT-TD can
provide a more reliable evaluation for models because of their high quality and
consistent annotations. Besides, they are more suitable for cross-domain
settings. Our experimental results show that in the cross-domain setting,
benchmark models trained with cleaned Open-Tables dataset can achieve
0.6\%-2.6\% higher weighted average F1 than the corresponding ones trained with
the noisy version of Open-Tables, demonstrating the reliability of the proposed
datasets. The datasets are public available.
