用框图信息指导表格结构生成，提升复杂场景识别准确率
Enhancing Table Structure Recognition via Bounding Box Guidance
- 先预测单元格框，再用框信息引导HTML序列生成
- 在5个基准数据集上达到当前最优性能
- 针对自然场景数据少问题，提出合成数据集SNSTab
表格结构识别（TSR）旨在从表格图像中提取单元格边界框和表格结构（如HTML）。尽管现有方法已取得显著进展，但最新图像到序列的方法在预测HTML序列时未显式利用边界框信息，导致复杂场景下错误率较高。本文提出一种新框架BGTR（框图引导的表格识别器），通过先预测单元格边界框，再用其引导HTML序列生成，有效提升准确性。针对自然场景表格数据量不足的问题，采用渐进式训练策略，并构建了合成自然场景表格数据集SNSTab。在五个基准数据集上的实验表明，该方法达到当前最优性能。
原文摘要 · Abstract (English)
Table Structure Recognition (TSR) aims to extract the bounding boxes of cells and table structure (e.g., HTML) from table images. Although current approaches have made significant progress, the latest image-to-sequence methods overlook the explicit utilization of the bounding box information when predicting HTML sequences, leading to error predictions in complex scenes. In this paper, we introduce a novel framework BGTR (Bounding Box-Guided Table Recognizer). To more effectively utilize bounding box information, we first predict the bounding boxes of cells and then use this information to guide the generation of HTML sequences. While utilizing bounding box information can enhance the accuracy of HTML sequences, for natural scene tables, the data volume is too small to allow for sufficient training of bbox-guided HTML generation. In response, we adopt a progressive training method for natural scene tables and introduce SNSTab, a synthetically generated natural scene table dataset. Our experiments on five benchmark datasets demonstrate SOTA performance.
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