%0 Journal Article
	%A Lin Cheng and  Zijiang Yang
	%D 2020
	%J International Journal of Cognitive and Language Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 162, 2020
	%T GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
	%U https://publications.waset.org/pdf/10011284
	%V 162
	%X 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.
	%P 230 - 235