基于视觉对齐顺序坐标建模的表格结构识别
Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling
摘要：
表格结构识别的目的是将非结构化表格图像的逻辑和物理结构提取为机器可读的格式。最新的端到端图像到文本方法通过两个解码器同时预测两个结构，其中物理结构（单元的边界框）的预测基于逻辑结构的表示。然而，以前的方法与不精确的边界框作斗争，因为逻辑表示缺乏局部视觉信息。为了解决这个问题，我们提出了一个端到端的顺序建模框架表结构识别称为VAST。它包含了一个新的坐标序列解码器触发的非空单元从逻辑结构解码器的表示。在坐标序列解码器中，我们将限界框坐标建模为语言序列，其中左、上、右和下坐标被顺序解码以利用坐标间依赖性。此外，我们提出了一个辅助的视觉对齐损失，以强制非空单元格的逻辑表示包含更多的本地视觉细节，这有助于产生更好的单元格边界框。大量的实验表明，我们提出的方法可以实现国家的最先进的结果，在逻辑和物理结构识别。消融研究也验证了所提出的坐标序列解码器和视觉对齐损失是我们的方法成功的关键。
英文摘要：
Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, the previous methods struggle with imprecise bounding boxes as the logical representation lacks local visual information. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
