Automating table extraction from documents is a tedious task that can now be streamlined with modern technology, including deep learning and computer vision. This process is beneficial for individuals, industries, and businesses that manage large volumes of tabular data, such as in invoices or forms, and it can significantly reduce manual labor and errors. The article highlights Nanonets' capabilities in efficiently extracting tabular data from various document types by employing deep learning techniques like the TableNet architecture and other neural network models such as DeepDeSRT and Graph Neural Networks. These methods offer enhanced accuracy in detecting and extracting table structures, overcoming traditional challenges of varied layouts, image quality, and content density. Additionally, the text discusses the limitations of traditional approaches and how advances in deep learning can address these issues, making table extraction more reliable and scalable across different formats and industries.