@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}, }