Evaluating Table Structure Recognition: A New Perspective
This work addresses a specific evaluation challenge in document analysis, but it is incremental as it builds on prior metrics.
The paper tackles the problem of evaluating table structure recognition algorithms by proposing a new metric, TEDS (IOU), which uses bounding boxes to address shortcomings in capturing text and empty cells alignment, and demonstrates its effectiveness through examples.
Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.