Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network

Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved using machines; however, machines encounter challenges in natural language processing and computer vision. Significant progress has improved existing methods in t...

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Bibliographic Details
Main Authors: Pengpeng Jian, Fucheng Guo, Cong Pan, Yanli Wang, Yangrui Yang, Yang Li
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Electronics
Subjects:
interpretable solving
GCN
RetinaNet
formal language
diagram parsing
Online Access:https://www.mdpi.com/2079-9292/12/22/4578

Internet

https://www.mdpi.com/2079-9292/12/22/4578

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