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Flowcharts and mind maps, collectively known as flowmind, are vital in dailyactivities, 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 conversionchallenges. Existing sketch recognition methods face limitations in practicalsituations, being field-specific and lacking digital conversion steps. Ourpaper introduces the Flowmind2digital method and hdFlowmind dataset to addressthese challenges. Flowmind2digital, utilizing neural networks and keypointdetection, achieves a record 87.3% accuracy on our dataset, surpassing previousmethods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowmindsacross 22 scenarios, outperforms existing datasets. Additionally, ourexperiments emphasize the importance of simple graphics, enhancing accuracy by9.3%.

Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach