Evolving paradigms in table structure recognition with deep neural networks
P Ila Chandana Kumari, R. Mohan, Sridhar Patthi · 2025
Tables play a ubiquitous role in diverse contexts, spanning scientific journals, papers, websites, newspapers, and even in the products that are purchased from supermarkets. The ability to detect tables is crucial for automatically deciphering indenture content. The realm of table discernment has witnessed significant advancements, largely propelled by the swift evolution of Automatic learning networks. The objectives of this in-depth learning encompass providing a comprehensive understanding of key approaches and future extensions in Table Discernment, offering insights into various procedure, and presenting a layout of different methods and their results. Additionally, analysis is conducted on the applications, both traditional and innovative, within the field. To assist readers, systematically organized datasets and source code from existing models, serving as a guide through the extensive literature. Furthermore, delve into the architecture, exploring the integration of diverse object identification and table border formation recognition methods to craft an effectual and efficacious system. Alongside, highlighted current development trends, ensuring alignment with cutting-edge algorithms and potential avenues for future research. In sum, this aims to be a navigational tool in the expansive landscape of Table Discernment, shedding light on its intricacies and usher in for advancements in the research.