GRCNN: Graph Recognition Convolutional Neural Network ... - ArXiv

Computer Science > Computer Vision and Pattern Recognition arXiv:2011.05980 (cs) [Submitted on 11 Nov 2020] Title:GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts Authors:Lin Cheng, Zijiang Yang View a PDF of the paper titled GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts, by Lin Cheng and 1 other authors View PDF

Abstract:Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specifications. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and nodes are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2011.05980 [cs.CV]
(or arXiv:2011.05980v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2011.05980 Focus to learn more arXiv-issued DOI via DataCite

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From: Lin Cheng [view email] [v1] Wed, 11 Nov 2020 18:52:25 UTC (639 KB) Full-text links:

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