  End-to-end text spotting has attached great attention recently due to its
benefits on global optimization and high maintainability for real applications.
However, the input scale has always been a tough trade-off since recognizing a
small text instance usually requires enlarging the whole image, which brings
high computational costs. In this paper, to address this problem, we propose a
novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting
framework, which aims to infer images in different small but recognizable
resolutions and achieve a better balance between accuracy and efficiency.
Concretely, we adopt a resolution selector to dynamically decide the input
resolutions for different images, which is constraint by both inference
accuracy and computational cost. Another sequential knowledge distillation
strategy is conducted on the text recognition branch, making the low-res input
obtains comparable performance to a high-res image. The proposed method can be
optimized end-to-end and adopted in any current text spotting framework to
improve the practicability. Extensive experiments on several text spotting
benchmarks show that the proposed method vastly improves the usability of
low-res models. The code is available at
https://github.com/hikopensource/DAVAR-Lab-OCR/.

Computer Vision and Pattern Recognition