Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
This addresses the need for efficient digital conversion of flowminds for practical applications, though it appears incremental as it builds on existing sketch recognition with specific improvements.
The paper tackles the problem of digitizing hand-drawn flowcharts and mind maps (flowminds) by introducing Flowmind2digital, a method that achieves 87.3% accuracy on a new dataset, surpassing previous methods by 11.9%.
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%.