Flowmind2Digital: The First Comprehensive Flowmind Recognition and
  Conversion Approach
Huanyu Liu Jianfeng Cai Tingjia Zhang Hongsheng Li Siyuan Wang Guangming Zhu Syed Afaq Ali Shah Mohammed Bennamoun Liang Zhang
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%.