  We propose a framework for sequence-to-sequence contrastive learning (SeqCLR)
of visual representations, which we apply to text recognition. To account for
the sequence-to-sequence structure, each feature map is divided into different
instances over which the contrastive loss is computed. This operation enables
us to contrast in a sub-word level, where from each image we extract several
positive pairs and multiple negative examples. To yield effective visual
representations for text recognition, we further suggest novel augmentation
heuristics, different encoder architectures and custom projection heads.
Experiments on handwritten text and on scene text show that when a text decoder
is trained on the learned representations, our method outperforms
non-sequential contrastive methods. In addition, when the amount of supervision
is reduced, SeqCLR significantly improves performance compared with supervised
training, and when fine-tuned with 100% of the labels, our method achieves
state-of-the-art results on standard handwritten text recognition benchmarks.
