  Table structure recognition is a crucial part of document image analysis
domain. Its difficulty lies in the need to parse the physical coordinates and
logical indices of each cell at the same time. However, the existing methods
are difficult to achieve both these goals, especially when the table splitting
lines are blurred or tilted. In this paper, we propose an accurate and
end-to-end transformer-based table structure recognition method, referred to as
TRUST. Transformers are suitable for table structure recognition because of
their global computations, perfect memory, and parallel computation. By
introducing novel Transformer-based Query-based Splitting Module and
Vertex-based Merging Module, the table structure recognition problem is
decoupled into two joint optimization sub-tasks: multi-oriented table
row/column splitting and table grid merging. The Query-based Splitting Module
learns strong context information from long dependencies via Transformer
networks, accurately predicts the multi-oriented table row/column separators,
and obtains the basic grids of the table accordingly. The Vertex-based Merging
Module is capable of aggregating local contextual information between adjacent
basic grids, providing the ability to merge basic girds that belong to the same
spanning cell accurately. We conduct experiments on several popular benchmarks
including PubTabNet and SynthTable, our method achieves new state-of-the-art
results. In particular, TRUST runs at 10 FPS on PubTabNet, surpassing the
previous methods by a large margin.
