  When handling complicated text images (e.g., irregular structures, low
resolution, heavy occlusion, and uneven illumination), existing supervised text
recognition methods are data-hungry. Although these methods employ large-scale
synthetic text images to reduce the dependence on annotated real images, the
domain gap still limits the recognition performance. Therefore, exploring the
robust text feature representations on unlabeled real images by self-supervised
learning is a good solution. However, existing self-supervised text recognition
methods conduct sequence-to-sequence representation learning by roughly
splitting the visual features along the horizontal axis, which limits the
flexibility of the augmentations, as large geometric-based augmentations may
lead to sequence-to-sequence feature inconsistency. Motivated by this, we
propose a novel self-supervised Character-to-Character Distillation method,
CCD, which enables versatile augmentations to facilitate general text
representation learning. Specifically, we delineate the character structures of
unlabeled real images by designing a self-supervised character segmentation
module. Following this, CCD easily enriches the diversity of local characters
while keeping their pairwise alignment under flexible augmentations, using the
transformation matrix between two augmented views from images. Experiments
demonstrate that CCD achieves state-of-the-art results, with average
performance gains of 1.38% in text recognition, 1.7% in text segmentation, 0.24
dB (PSNR) and 0.0321 (SSIM) in text super-resolution. Code is available at
https://github.com/TongkunGuan/CCD.
