Table Structure

Table structure recognition (TSR) focuses on automatically extracting and interpreting tabular data from diverse sources, such as scanned documents and electronic health records, to enable efficient data processing and analysis. Current research emphasizes end-to-end approaches using deep learning models, including transformers and object detection networks like Faster R-CNN and CornerNet, often incorporating techniques like graph representations and multi-task learning to handle complex table structures and diverse data types. These advancements are crucial for improving information retrieval, knowledge graph construction, and data analysis across various fields, particularly in domains with large volumes of unstructured tabular data. The development of robust and accurate TSR methods is essential for unlocking the potential of this ubiquitous data format.