Sequential visual and semantic consistency for semi-supervised text recognition

Mingkun Yang, Biao Yang, Minghui Liao, Yingying Zhu, Xiang Bai

Abstract: Highlights•Semi-supervised learning enhances practical text recognition significantly.•Consistency Regularization-based methods are efficient and effective.•Dynamic programming ensures word-level visual consistency and improves performance.•Word-level semantic consistency improves performance via reinforcement learning.•Integrating multi-level and multi-modal consistency regularization works better.