Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning. Specifically, a modified instance segmentation method is proposed to extract geometric primitives, and the graph neural network (GNN) is leveraged to realize relation parsing and primitive classification incorporating geometric features and prior knowledge. All the modules are integrated into an end-to-end model called PGDPNet to perform all the sub-tasks simultaneously. In addition, we build a new large-scale geometry diagram dataset named PGDP5K with primitive level annotations. Experiments on PGDP5K and an existing dataset IMP-Geometry3K show that our model outperforms state-of-the-art methods in four sub-tasks remarkably. Our code, dataset and appendix material are available at https://github.com/mingliangzhang2018/PGDP.

翻译：几何图解析在几何问题解决中起着关键作用, 即原始提取和关系解析由于复杂的布局和原始关系, 仍然具有挑战性。 在本文中, 我们根据深层次的学习和图形推理, 提出了一个强大的图表解析器。 具体地说, 提议了一种修改的例分解法来提取几何原始, 图形神经网络( GNN) 用于实现包含几何特征和先前知识的关系解析和原始分类。 所有模块都被纳入一个端到端模型, 称为 PGDPNet, 以同时执行所有子任务。 此外, 我们建立了一个名为 PGDP5K 的大型几何图数据集, 并配有原始层次的描述。 PGDP5K 实验和现有的数据集 IMP- Geography3K 显示, 我们的模型在四个子卫星中超越了状态方法。 我们的代码、 数据集和附录材料可在 https://github.com/ mingliangzhangh2018/ PGDP 上查阅 。