Papers
1
Total Citations
22
H-Index
1
About
Ze-Yu Zuo is a researcher whose work focuses on the challenging domain of scene text detection and extraction from video, a critical area for robotics, autonomous systems, and human-computer interaction. His major contribution lies in advancing methods that integrate both local and global information to overcome the inherent difficulties of video text, such as heterogeneous backgrounds, varied fonts, nonuniform illumination, and motion blur. His most cited paper, "Scene text detection in video by learning locally and globally" (2016), with 22 citations, proposes a novel framework that moves beyond traditional local-only approaches, significantly improving detection robustness in complex, dynamic environments. This work addresses a key bottleneck in enabling machines to read text from real-world video streams, impacting applications from assistive technology to automated surveillance. Zuo’s research is notable for its practical focus on bridging the gap between controlled settings and the unpredictable conditions of real-world video, making his contributions valuable for both academic study and industrial deployment in computer vision.
Research Focus
Key Achievements
Top Papers
- 1Scene text detection in video by learning locally and globally22 citations · 2016