One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

TL;DR

This paper introduces a one-stage low-resolution text recognition method that transfers high-resolution knowledge to improve accuracy and efficiency, avoiding the need for separate super-resolution steps.

Contribution

It proposes a novel knowledge distillation framework with multi-level transfer, including visual focus, semantic contrastive, and soft logits losses, to enhance low-resolution text recognition.

Findings

Outperforms two-stage super-resolution methods in accuracy and efficiency

Demonstrates robustness across various low-resolution scenarios

Provides a practical, end-to-end recognition pipeline

Abstract

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.