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：本文提出一种基于深度学习的新型文档词语聚类方法，并将其应用于OCR输出结果的表格检测与识别任务。我们将表格结构自底向上地解释为词语对之间关系的图结构（包括同行、同列、同表头及同表关系），并采用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数据集上验证了本方法的性能。相较于DETR、Faster R-CNN等当前最先进的检测方法，本方法在保持相当或更高准确率的同时，所需模型规模显著减小。