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Recent deep learning approaches in table detection achieved outstandingperformance 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 oftraining cases, and samples quality. In this paper, we introduce a diverselarge-scale dataset for table detection with more than seven thousand samplescontaining a wide variety of table structures collected from many diversesources. In addition to that, we also present baseline results using aconvolutional neural network-based method to detect table structure indocuments. Experimental results show the superiority of applying convolutionaldeep learning methods over classical computer vision-based methods. Theintroduction of this diverse table detection dataset will enable the communityto develop high throughput deep learning methods for understanding documentlayout and tabular data processing. Dataset is available at: 1.https://www.kaggle.com/datasets/mrinalim/stdw-dataset 2.https://huggingface.co/datasets/n3011/STDW

Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method