  Table structure recognition is a challenging task due to the various
structures and complicated cell spanning relations. Previous methods handled
the problem starting from elements in different granularities (rows/columns,
text regions), which somehow fell into the issues like lossy heuristic rules or
neglect of empty cell division. Based on table structure characteristics, we
find that obtaining the aligned bounding boxes of text region can effectively
maintain the entire relevant range of different cells. However, the aligned
bounding boxes are hard to be accurately predicted due to the visual
ambiguities. In this paper, we aim to obtain more reliable aligned bounding
boxes by fully utilizing the visual information from both text regions in
proposed local features and cell relations in global features. Specifically, we
propose the framework of Local and Global Pyramid Mask Alignment, which adopts
the soft pyramid mask learning mechanism in both the local and global feature
maps. It allows the predicted boundaries of bounding boxes to break through the
limitation of original proposals. A pyramid mask re-scoring module is then
integrated to compromise the local and global information and refine the
predicted boundaries. Finally, we propose a robust table structure recovery
pipeline to obtain the final structure, in which we also effectively solve the
problems of empty cells locating and division. Experimental results show that
the proposed method achieves competitive and even new state-of-the-art
performance on several public benchmarks.
