  Recognizing characters from low-resolution (LR) text images poses a
significant challenge due to the information deficiency as well as the noise
and blur in low-quality images. Current solutions for low-resolution text
recognition (LTR) typically rely on a two-stage pipeline that involves
super-resolution as the first stage followed by the second-stage recognition.
Although this pipeline is straightforward and intuitive, it has to use an
additional super-resolution network, which causes inefficiencies during
training and testing. Moreover, the recognition accuracy of the second stage
heavily depends on the reconstruction quality of the first stage, causing
ineffectiveness. In this work, we attempt to address these challenges from a
novel perspective: adapting the recognizer to low-resolution inputs by
transferring the knowledge from the high-resolution. Guided by this idea, we
propose an efficient and effective knowledge distillation framework to achieve
multi-level knowledge transfer. Specifically, the visual focus loss is proposed
to extract the character position knowledge with resolution gap reduction and
character region focus, the semantic contrastive loss is employed to exploit
the contextual semantic knowledge with contrastive learning, and the soft
logits loss facilitates both local word-level and global sequence-level
learning from the soft teacher label. Extensive experiments show that the
proposed one-stage pipeline significantly outperforms super-resolution based
two-stage frameworks in terms of effectiveness and efficiency, accompanied by
favorable robustness. Code is available at https://github.com/csguoh/KD-LTR.
