@article{(Open Science Index):https://publications.waset.org/pdf/10011284,
	  title     = {GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts},
	  author    = {Lin Cheng and  Zijiang Yang},
	  country	= {},
	  institution	= {},
	  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 specification. 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 node are 94.1% and 67.9%,
respectively. On average, it takes about 60 milliseconds to synthesize
a program.},
	    journal   = {International Journal of Cognitive and Language Sciences},
	  volume    = {14},
	  number    = {6},
	  year      = {2020},
	  pages     = {230 - 235},
	  ee        = {https://publications.waset.org/pdf/10011284},
	  url   	= {https://publications.waset.org/vol/162},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 162, 2020},