Formerge: Recover Spanning Cells in Complex Table Structure Using Transformer Network.

Table structure recognition (TSR) task is indispensable in a robust document analysis system. Recently, the split-and-merge-based approach has attracted many researchers to develop the TSR problem. It is a two-stage method: firstly, split table region into row/column separation and obtain grid cells of the table; then recover spanning cells by merging some grid cells and complete the table structure. Most recent proposals focus on the first stage, with few solutions for the merge task. Therefore, this paper proposes a novel method to recover spanning cells using Transformer networks called Formerge. This model contains a Transformer encoder and two parallel left-right/top-down decoders. With grid structure output from a split branch, Formerge extracts cell features with RoIAlign and passes them into the encoder to enhance features before decoding to detect spanning cells. Our technique outperforms other methods on two benchmark datasets, including SciTSR and ICDAR19-cTDaR modern.