Image-based table recognition is a challenging task due to the diversity of table styles and the complexity of table structures. Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems. In this paper, we propose an end-to-end multi-task learning model for image-based table recognition. The proposed model consists of one shared encoder, one shared decoder, and three separate decoders which are used for learning three sub-tasks of table recognition: table structure recognition, cell detection, and cell-content recognition. The whole system can be easily trained and inferred in an end-to-end approach. In the experiments, we evaluate the performance of the proposed model on two large-scale datasets: FinTabNet and PubTabNet. The experiment results show that the proposed model outperforms the state-of-the-art methods in all benchmark datasets.

翻译：基于图像的表格识别是一项具有挑战性的任务，原因在于表格样式的多样性和表格结构的复杂性。以往的大多数方法采用非端到端的方式，将问题分解为两个独立的子问题：表格结构识别与单元格内容识别，并分别使用两个独立的系统来处理每个子问题。本文提出了一种用于图像表格识别的端到端多任务学习模型。该模型由一个共享编码器、一个共享解码器以及三个独立解码器组成，分别用于学习表格识别的三个子任务：表格结构识别、单元格检测和单元格内容识别。整个系统可以通过端到端的方式轻松训练和推理。在实验中，我们在两个大规模数据集（FinTabNet和PubTabNet）上评估了所提模型的性能。实验结果表明，该模型在所有基准数据集上均优于现有最先进方法。