Text Spotting
Text spotting aims to automatically locate and recognize text within images and videos, a crucial task for various applications like document processing and autonomous navigation. Current research emphasizes improving accuracy and efficiency, particularly focusing on weakly supervised learning methods that reduce the need for expensive manual annotations, and exploring transformer-based architectures and novel loss functions for enhanced performance. These advancements are driving progress in areas such as cross-domain generalization, handling noisy or deformed text, and enabling applications in diverse domains including historical manuscript analysis and robotics.
Papers
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization
Yuliang Liu, Mingxin Huang, Hao Yan, Linger Deng, Weijia Wu, Hao Lu, Chunhua Shen, Lianwen Jin, Xiang Bai
FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-motor Robot Manipulation Skills
Yongqiang Zhao, Kun Qian, Boyi Duan, Shan Luo