RCAM-Transformer: A Novel Approach to Table Reconstruction Using Row-Column Attention Mechanism

Table reconstruction, a critical task in the field of table structure recognition (TSR), plays a vital role in various domains, such as data mining, machine learning, and information retrieval. While many existing TSR methods employ transformer-based models with generally impressive performance, a gap remains in transformer models specifically designed to handle the distinct attributes of table rows and columns. Moreover, there is a lack of robust table reconstruction strategies based on object detection models. To address these issues, we introduce the Row-Column Attention Mechanism (RCAM). When combined with a transformer model and integrated with partial global attention, it forms the RCAM-Transformer. This model is tailored to effectively process the unique properties of tabular data. In addition, we have developed a novel table reconstruction strategy that leverages object detection models, which improves the recognition and treatment of tabular data. Our experiments, conducted using the PubTables-1M and FinTabNet dataset, along with our self-constructed Annual Report TableSet, not only validated the effectiveness of the RCAM but also demonstrated the improved accuracy of table reconstruction with the use of our RCAM-Transformer. Such outcomes highlight the potential of the RCAM-Transformer to advance table extraction in various fields.