一次性解析表格空间与逻辑结构，速度更快且精度更高。
TableCenterNet: A one-stage network for table structure recognition
- 将表格的布局和逻辑结构统一为并行回归任务
- 在TableGraph-24k数据集上达到当前最优性能
- 无需复杂后处理，适合实际应用部署
表格结构识别旨在将非结构化数据中的表格解析为机器可理解的格式。现有方法多采用两阶段流程或优化的一阶段方案，前者需串联多个网络训练并进行耗时的序列解码，后者依赖复杂的后处理算法来推断表格逻辑结构，难以兼顾跨场景适应性、鲁棒性与计算效率。本文提出一种端到端的一阶段表格结构解析网络TableCenterNet，首次将表格的空间结构与逻辑结构预测统一为并行回归任务，并通过共享特征提取层与任务专用解码器的协同架构，隐式学习单元格的空间-逻辑位置映射规律。相比两阶段方法，本方法更易训练且推理更快。在基准数据集上的实验表明，TableCenterNet能有效解析多样化场景下的表格结构，在TableGraph-24k数据集上达到领先性能。代码已开源。
原文摘要 · Abstract (English)
Table structure recognition aims to parse tables in unstructured data into machine-understandable formats. Recent methods address this problem through a two-stage process or optimized one-stage approaches. However, these methods either require multiple networks to be serially trained and perform more time-consuming sequential decoding, or rely on complex post-processing algorithms to parse the logical structure of tables. They struggle to balance cross-scenario adaptability, robustness, and computational efficiency. In this paper, we propose a one-stage end-to-end table structure parsing network called TableCenterNet. This network unifies the prediction of table spatial and logical structure into a parallel regression task for the first time, and implicitly learns the spatial-logical location mapping laws of cells through a synergistic architecture of shared feature extraction layers and task-specific decoding. Compared with two-stage methods, our method is easier to train and faster to infer. Experiments on benchmark datasets show that TableCenterNet can effectively parse table structures in diverse scenarios and achieve state-of-the-art performance on the TableGraph-24k dataset. Code is available at https://github.com/dreamy-xay/TableCenterNet.
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