Supervised Text Recognition

Supervised text recognition aims to train computer models to accurately transcribe text from images, a task crucial for various applications. Current research emphasizes overcoming limitations of existing supervised methods, particularly the need for large labeled datasets, by exploring semi-supervised and self-supervised learning techniques. These approaches leverage unlabeled data through methods like pseudo-labeling, contrastive learning, and masked image modeling, often incorporating both visual and semantic consistency checks to improve robustness and accuracy. Advances in this field have significant implications for automating document processing, improving accessibility for visually impaired individuals, and enhancing medical image analysis.