Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
This work addresses the need for more diverse and high-quality training data for table detection, which is crucial for improving document layout understanding and tabular data processing in the community.
The authors tackled the problem of limited diversity in existing table detection datasets by introducing a new large-scale dataset with over 7,000 diverse samples from various sources, and they demonstrated that convolutional neural network-based methods outperform classical computer vision approaches in detecting tables.
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