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%.
@article{arxiv.2401.03742,
title = {Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach},
author = {Huanyu Liu and Jianfeng Cai and Tingjia Zhang and Hongsheng Li and Siyuan Wang and Guangming Zhu and Syed Afaq Ali Shah and Mohammed Bennamoun and Liang Zhang},
journal= {arXiv preprint arXiv:2401.03742},
year = {2024}
}