Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

• URL: http://arxiv.org/abs/2401.03742v1
• Date: Mon, 8 Jan 2024 09:05:20 GMT
• Title: Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
• Authors: Huanyu Liu, Jianfeng Cai, Tingjia Zhang, Hongsheng Li, Siyuan Wang, Guangming Zhu, Syed Afaq Ali Shah, Mohammed Bennamoun and Liang Zhang
• Abstract summary: Flowcharts and mind maps, collectively known as flowmind, are vital in daily activities, with hand-drawn versions facilitating real-time collaboration. 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.
• Score: 57.00892368627367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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