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The first phase of table recognition is to detect the tabular area in adocument. Subsequently, the tabular structures are recognized in the secondphase in order to extract information from the respective cells. Tabledetection and structural recognition are pivotal problems in the domain oftable understanding. However, table analysis is a perplexing task due to thecolossal amount of diversity and asymmetry in tables. Therefore, it is anactive area of research in document image analysis. Recent advances in thecomputing capabilities of graphical processing units have enabled deep neuralnetworks to outperform traditional state-of-the-art machine learning methods.Table understanding has substantially benefited from the recent breakthroughsin deep neural networks. However, there has not been a consolidated descriptionof the deep learning methods for table detection and table structurerecognition. This review paper provides a thorough analysis of the modernmethodologies that utilize deep neural networks. This work provided a thoroughunderstanding of the current state-of-the-art and related challenges of tableunderstanding in document images. Furthermore, the leading datasets and theirintricacies have been elaborated along with the quantitative results. Moreover,a brief overview is given regarding the promising directions that can serve asa guide to further improve table analysis in document images.

Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks