# GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts

arXiv: https://arxiv.org/abs/2011.05980 · HTML: https://mlsanity.com/paper/2011.05980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding](https://mlsanity.com/paper/2505.07864/) (2025)

## Abstract (arXiv)

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.