🤖 AI Summary
To address the instability in bounding-box prediction and the trade-off between accuracy and efficiency in large-scale, dense table structure recognition, this paper proposes TABLET—a pure-encoder Transformer architecture. It introduces a novel Split-Merge two-stage paradigm: the Split stage formulates row/column segmentation as a sequence labeling task, while the Merge stage casts cell merging as a grid-cell classification task—fully eliminating bounding-box regression. TABLET comprises dual encoders for splitting and a single encoder for merging, requiring no detection head, thereby achieving high accuracy, low latency, and strong robustness. On FinTabNet and PubTabNet, it significantly outperforms state-of-the-art methods in both accuracy and inference speed, with substantially reduced computational overhead. Its lightweight design and end-to-end differentiability enable industrial-scale deployment for real-world table understanding tasks.
📝 Abstract
To address the challenges of table structure recognition, we propose a novel Split-Merge-based top-down model optimized for large, densely populated tables. Our approach formulates row and column splitting as sequence labeling tasks, utilizing dual Transformer encoders to capture feature interactions. The merging process is framed as a grid cell classification task, leveraging an additional Transformer encoder to ensure accurate and coherent merging. By eliminating unstable bounding box predictions, our method reduces resolution loss and computational complexity, achieving high accuracy while maintaining fast processing speed. Extensive experiments on FinTabNet and PubTabNet demonstrate the superiority of our model over existing approaches, particularly in real-world applications. Our method offers a robust, scalable, and efficient solution for large-scale table recognition, making it well-suited for industrial deployment.