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%.

翻译：流程图与思维导图（统称flowmind）在日常活动中至关重要，手绘版本支持实时协作。然而，为高效处理，将其数字化的需求日益增长。自动转化方法对于克服人工转化挑战至关重要。现有草图识别方法在实际场景中存在局限，如领域特定且缺乏数字转化步骤。本文提出Flowmind2digital方法与hdFlowmind数据集以应对这些挑战。Flowmind2digital利用神经网络与关键点检测，在我们的数据集上达到创纪录的87.3%准确率，超越此前方法11.9%。hdFlowmind数据集包含22个场景中的1,776个标注flowmind，优于现有数据集。此外，实验强调了简单图形的重要性，使准确率提升9.3%。