  The first phase of table recognition is to detect the tabular area in a
document. Subsequently, the tabular structures are recognized in the second
phase in order to extract information from the respective cells. Table
detection and structural recognition are pivotal problems in the domain of
table understanding. However, table analysis is a perplexing task due to the
colossal amount of diversity and asymmetry in tables. Therefore, it is an
active area of research in document image analysis. Recent advances in the
computing capabilities of graphical processing units have enabled deep neural
networks to outperform traditional state-of-the-art machine learning methods.
Table understanding has substantially benefited from the recent breakthroughs
in deep neural networks. However, there has not been a consolidated description
of the deep learning methods for table detection and table structure
recognition. This review paper provides a thorough analysis of the modern
methodologies that utilize deep neural networks. This work provided a thorough
understanding of the current state-of-the-art and related challenges of table
understanding in document images. Furthermore, the leading datasets and their
intricacies have been elaborated along with the quantitative results. Moreover,
a brief overview is given regarding the promising directions that can serve as
a guide to further improve table analysis in document images.
