  Recent deep learning approaches in table detection achieved outstanding
performance and proved to be effective in identifying document layouts.
Currently, available table detection benchmarks have many limitations,
including the lack of samples diversity, simple table structure, the lack of
training cases, and samples quality. In this paper, we introduce a diverse
large-scale dataset for table detection with more than seven thousand samples
containing a wide variety of table structures collected from many diverse
sources. In addition to that, we also present baseline results using a
convolutional neural network-based method to detect table structure in
documents. Experimental results show the superiority of applying convolutional
deep learning methods over classical computer vision-based methods. The
introduction of this diverse table detection dataset will enable the community
to develop high throughput deep learning methods for understanding document
layout and tabular data processing. Dataset is available at: 1.
https://www.kaggle.com/datasets/mrinalim/stdw-dataset 2.
https://huggingface.co/datasets/n3011/STDW
