  Flowcharts and mind maps, collectively known as flowmind, are vital in daily
activities, with hand-drawn versions facilitating real-time collaboration.
However, there's a growing need to digitize them for efficient processing.
Automated conversion methods are essential to overcome manual conversion
challenges. Existing sketch recognition methods face limitations in practical
situations, being field-specific and lacking digital conversion steps. Our
paper introduces the Flowmind2digital method and hdFlowmind dataset to address
these challenges. Flowmind2digital, utilizing neural networks and keypoint
detection, achieves a record 87.3% accuracy on our dataset, surpassing previous
methods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowminds
across 22 scenarios, outperforms existing datasets. Additionally, our
experiments emphasize the importance of simple graphics, enhancing accuracy by
9.3%.
