A seven-stage OCR plus arrow-detection prompt pipeline lifts GPT-4o flowchart QA accuracy from 80% to 89% on 90 self-annotated questions, without task-specific fine-tuning.
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
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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.
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Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding
A seven-stage OCR plus arrow-detection prompt pipeline lifts GPT-4o flowchart QA accuracy from 80% to 89% on 90 self-annotated questions, without task-specific fine-tuning.