  Until recently, the number of public real-world text images was insufficient
for training scene text recognizers. Therefore, most modern training methods
rely on synthetic data and operate in a fully supervised manner. Nevertheless,
the amount of public real-world text images has increased significantly lately,
including a great deal of unlabeled data. Leveraging these resources requires
semi-supervised approaches; however, the few existing methods do not account
for vision-language multimodality structure and therefore suboptimal for
state-of-the-art multimodal architectures. To bridge this gap, we present
semi-supervised learning for multimodal text recognizers (SemiMTR) that
leverages unlabeled data at each modality training phase. Notably, our method
refrains from extra training stages and maintains the current three-stage
multimodal training procedure. Our algorithm starts by pretraining the vision
model through a single-stage training that unifies self-supervised learning
with supervised training. More specifically, we extend an existing visual
representation learning algorithm and propose the first contrastive-based
method for scene text recognition. After pretraining the language model on a
text corpus, we fine-tune the entire network via a sequential, character-level,
consistency regularization between weakly and strongly augmented views of text
images. In a novel setup, consistency is enforced on each modality separately.
Extensive experiments validate that our method outperforms the current training
schemes and achieves state-of-the-art results on multiple scene text
recognition benchmarks.
