Dinh Do Van
Papers
1
Total Citations
3
H-Index
1
About
Dinh Do Van is a researcher at the forefront of human-computer interaction, with a primary focus on hand gesture recognition using deep learning. His most cited work introduces the TQU-HG dataset, a specialized resource for RGB-based hand gesture recognition, which he developed alongside a comparative study of deep learning models. This contribution addresses critical challenges in HCI and human-robot interaction, as well as assistive technologies for the deaf and mute communities. By providing a standardized benchmark and evaluating state-of-the-art architectures, Van’s research has laid essential groundwork for more robust, real-world gesture recognition systems. His work has already garnered attention, with his leading paper accumulating citations shortly after publication in 2024, signaling its growing impact. Van’s dedication to creating accessible, high-performance models underscores his commitment to bridging the gap between complex AI systems and practical, inclusive applications. For students and researchers exploring the intersection of computer vision and assistive technology, his studies offer both foundational datasets and methodological insights that continue to shape the field.
Research Focus
Key Achievements
Top Papers
- 1