Problem
Research questions and friction points this paper is trying to address.
Table Structure Recognition
Bounding Box
HTML Sequence
Natural Scene Tables
Enhancing Table Structure Recognition via Bounding Box Guidance
🤖 AI Summary
本文提出BGTR框架，通过预测单元格边界框并利用该信息指导HTML序列生成，提高表格结构识别精度，并采用渐进训练法和合成数据集SNSTab解决自然场景下数据量不足问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bounding Box-Guided
Table Structure Recognition
Progressive Training
Synthetic Dataset
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