Plane Geometry Diagram Parsing

Plane Geometry Diagram Parsing

Ming-Liang Zhang, Fei Yin, Yi-Han Hao, Cheng-Lin Liu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1636-1643. https://doi.org/10.24963/ijcai.2022/228

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.
Keywords:
Computer Vision: Scene analysis and understanding   
Computer Vision: Recognition (object detection, categorization)
Computer Vision: Segmentation
Computer Vision: Visual reasoning and symbolic representation
Multidisciplinary Topics and Applications: Education