Plane Geometry Diagram Parsing

19 May 2022  ·  Ming-Liang Zhang, Fei Yin, Yi-Han Hao, Cheng-Lin Liu ·

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.

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Datasets


Introduced in the Paper:

PGDP5K

Used in the Paper:

Geometry3K GeoS

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Mathematical Question Answering Geometry3K PGDPNet Accuracy (%) 74.1 # 3
Scene Parsing PGDP5K PGDPNet Total Accuracy 84.7 # 1

Methods