This work presents a systematic review of data augmentation and generation techniques for offline handwritten text recognition. The paper surveys the state of the art in augmentation strategies, generative models, and synthetic data pipelines used to improve recognition robustness, accuracy, and generalisation, with a particular focus on handwritten text scenarios.
Rassul, Yassin Hussein, Aram M. Ahmed, Polla Fattah, Bryar A. Hassan, Arwaa W. Abdulkareem, Tarik A. Rashid, and Joan Lu. “Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques.” arXiv preprint arXiv:2507.06275 (2025).