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

TL;DR

This paper reviews recent deep neural network methods for table detection and structure recognition in document images, highlighting current challenges, datasets, and future research directions.

Contribution

It provides a comprehensive analysis of deep learning approaches for table recognition, consolidating recent advancements and identifying key challenges and datasets.

Findings

Deep neural networks outperform traditional methods in table detection.

Current datasets have limitations in diversity and complexity.

Future directions include improving model robustness and dataset quality.

Abstract

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.