Image-Based Relation Classification Approach for Table Structure Recognition

In recent years, the use of tabular data has become a major area of research and development. However, the number of tables structured in a machine-readable format is still limited. A major challenge that is encountered when using tabular data is converting the table information in a free-format document into a structured format. Unlike markup languages such as HTML, XML, and JSON, free-format documents such as PDF, Word, Excel, and images generally have no tags or separators. Therefore, the table structure should be recognized from the positional information of the table elements. A major approach of table structure recognition is to classify the relationship between each pair of bounding boxes of the table elements. Recent works have achieved significant improvements by applying graph convolutional networks (GCNs) to the graph structure of the bounding boxes. However, fully recognizing a complex table structure is still a major challenge, owing to the presence of spanning cells. In this study, we propose a novel, simple image-based approach to this relation classification task. Our model efficiently exploits information such as the geometry of the table elements and ruled lines through an image cropping strategy based on the pairs of bounding boxes. We evaluate our approach on two real-world table datasets by comparing four baselines including two state-of-the-art GCN approaches. We observe that our approach significantly outperforms the baseline in the exact matching ratio for tables by up to 6.7%.