Table structure recognition is an indispensable element for enabling machines to comprehend tables. Its primary purpose is to identify the internal structure of a table. Nevertheless, due to the complexity and diversity of their structure and style, it is highly challenging to parse the tabular data into a structured format that machines can comprehend. In this work, we adhere to the principle of the split-and-merge based methods and propose an accurate table structure recognizer, termed SEMv2 (SEM: Split, Embed and Merge). Unlike the previous works in the ``split'' stage, we aim to address the table separation line instance-level discrimination problem and introduce a table separation line detection strategy based on conditional convolution. Specifically, we design the ``split'' in a top-down manner that detects the table separation line instance first and then dynamically predicts the table separation line mask for each instance. The final table separation line shape can be accurately obtained by processing the table separation line mask in a row-wise/column-wise manner. To comprehensively evaluate the SEMv2, we also present a more challenging dataset for table structure recognition, dubbed iFLYTAB, which encompasses multiple style tables in various scenarios such as photos, scanned documents, etc. Extensive experiments on publicly available datasets (e.g. SciTSR, PubTabNet and iFLYTAB) demonstrate the efficacy of our proposed approach. The code and iFLYTAB dataset will be made publicly available upon acceptance of this paper.

翻译：表格结构识别是使机器理解表格不可或缺的要素，其核心目标在于识别表格的内部结构。然而，由于表格在结构与样式上的复杂性和多样性，将表格数据解析为机器可理解的结构化格式极具挑战性。本文遵循“分割-嵌入-合并”类方法的原则，提出了一种精确的表格结构识别模型，名为SEMv2（SEM：分割、嵌入与合并）。与以往在“分割”阶段的工作不同，我们致力于解决表格分隔线实例级判别问题，并引入了一种基于条件卷积的表格分隔线检测策略。具体而言，我们以自上而下的方式设计“分割”过程：首先检测表格分隔线实例，随后为每个实例动态预测其表格分隔线掩膜。通过按行/列方向处理表格分隔线掩膜，可精确获取最终的表格分隔线形状。为全面评估SEMv2，我们还提出了一个更具挑战性的表格结构识别数据集——iFLYTAB，该数据集包含照片、扫描文档等多种场景下的多风格表格。在公开数据集（如SciTSR、PubTabNet及iFLYTAB）上的大量实验验证了我们方法的有效性。代码与iFLYTAB数据集将在论文接收后公开。