We present a novel deep-learning-based method to cluster words in documents which we apply to detect and recognize tables given the OCR output. We interpret table structure bottom-up as a graph of relations between pairs of words (belonging to the same row, column, header, as well as to the same table) and use a transformer encoder model to predict its adjacency matrix. We demonstrate the performance of our method on the PubTables-1M dataset as well as PubTabNet and FinTabNet datasets. Compared to the current state-of-the-art detection methods such as DETR and Faster R-CNN, our method achieves similar or better accuracy, while requiring a significantly smaller model.

翻译：我们提出了一种基于深度学习的新方法，用于对文档中的词语进行聚类，并将其应用于根据OCR输出结果检测和识别表格。我们将表格结构自下而上地解释为词语对之间（属于同一行、同一列、表头以及同一表格）的关系图，并使用Transformer编码器模型预测其邻接矩阵。我们在PubTables-1M数据集以及PubTabNet和FinTabNet数据集上展示了该方法的表现。与当前最先进的检测方法（如DETR和Faster R-CNN）相比，我们的方法在实现相似或更高精度的同时，所需模型规模显著更小。