用分层回归方法提升表格结构识别速度与精度
SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition
- 通过双阶段Transformer结构逐级预测表格分割线
- 平均达25.6帧/秒，性能媲美当前最优模型
- 适合需要实时处理表格图像的场景
从图像数据中自动重建表格的逻辑布局，即表格结构识别（TSR），是语义数据提取的基础。每张表格由垂直和水平分割线构成。本文提出SepFormer，将分治合并范式融入单步分割线回归，采用类似DETR的架构，提升速度与鲁棒性。该方法为粗到精框架，通过两层Transformer解码器，从单行线逐步推导出线段组。粗粒度阶段通过额外角度损失逐步优化单行片段；细粒度阶段则对每个单行段采样点进行精修，生成线段组。SepFormer在SciTSR、PubTabNet、WTW和iFLYTAB等多个基准数据集上表现优异，平均运行速度达25.6 FPS。
原文摘要 · Abstract (English)
The automated reconstruction of the logical arrangement of tables from image data, termed Table Structure Recognition (TSR), is fundamental for semantic data extraction. Recently, researchers have explored a wide range of techniques to tackle this problem, demonstrating significant progress. Each table is a set of vertical and horizontal separators. Following this realization, we present SepFormer, which integrates the split-and-merge paradigm into a single step through separator regression with a DETR-style architecture, improving speed and robustness. SepFormer is a coarse-to-fine approach that predicts table separators from single-line to line-strip separators with a stack of two transformer decoders. In the coarse-grained stage, the model learns to gradually refine single-line segments through decoder layers with additional angle loss. At the end of the fine-grained stage, the model predicts line-strip separators by refining sampled points from each single-line segment. Our SepFormer can run on average at 25.6 FPS while achieving comparable performance with state-of-the-art methods on several benchmark datasets, including SciTSR, PubTabNet, WTW, and iFLYTAB.
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