LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment

by   Liang Qiao, et al.

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.


Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling

Table structure recognition aims to extract the logical and physical str...

Evaluating Table Structure Recognition: A New Perspective

Existing metrics used to evaluate table structure recognition algorithms...

A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training

Most existing Re-IDentification (Re-ID) methods are highly dependent on ...

Table Structure Recognition with Conditional Attention

Tabular data in digital documents is widely used to express compact and ...

ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene

Arbitrary-oriented text detection in the wild is a very challenging task...

Learning Markov Clustering Networks for Scene Text Detection

A novel framework named Markov Clustering Network (MCN) is proposed for ...

Robust Partial Matching for Person Search in the Wild

Various factors like occlusions, backgrounds, etc., would lead to misali...

Please sign up or login with your details

Forgot password? Click here to reset