论文标题
平面几何图解析
Plane Geometry Diagram Parsing
论文作者
论文摘要
几何图解析在几何问题解决中起关键作用，其中原始提取和关系解析由于复杂的布局和主要关系之间的关系而保持挑战。在本文中，我们根据深度学习和图形推理提出了一个强大的图表解析器。具体而言，提出了一种修改的实例分割方法来提取几何原料，并利用图形神经网络（GNN）来实现关系解析和原始分类，并结合了几何特征和先验知识。所有模块都集成到称为PGDPNET的端到端模型中，以同时执行所有子任务。此外，我们构建了一个具有原始级别注释的新的大规模几何图数据集。在PGDP5K和现有数据集Imp-Ageometry3K上进行的实验表明，我们的模型在四个子任务中的最先进方法非常明显。我们的代码，数据集和附录材料可在https://github.com/mingliangzhang2018/pgdp上找到。
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.
