TL;DR
This paper introduces Cycle-CenterNet, a novel method for parsing complex, real-world table structures from images with severe deformation, supported by a new large-scale dataset called WTW.
Contribution
We propose Cycle-CenterNet with a cycle-pairing module for improved table structure parsing in challenging real-world scenarios and introduce the WTW dataset for benchmarking.
Findings
Cycle-CenterNet achieves 24.6% higher accuracy on WTW dataset.
The cycle-pairing module improves detection and grouping of table cells.
Our method outperforms existing approaches on real-world table images.
Abstract
This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions. For designing such a system, we propose an approach named Cycle-CenterNet on the top of CenterNet with a novel cycle-pairing module to simultaneously detect and group tabular cells into structured tables. In the cycle-pairing module, a new pairing loss function is proposed for the network training. Alongside with our Cycle-CenterNet, we also present a large-scale dataset, named Wired Table in the Wild (WTW), which includes well-annotated structure parsing of multiple style tables in…
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Taxonomy
MethodsCycle-CenterNet · Deep Layer Aggregation · Batch Normalization · Convolution · Center Pooling · Cascade Corner Pooling · CenterNet
