  While scene text image super-resolution (STISR) has yielded remarkable
improvements in accurately recognizing scene text, prior methodologies have
placed excessive emphasis on optimizing performance, rather than paying due
attention to efficiency - a crucial factor in ensuring deployment of the
STISR-STR pipeline. In this work, we propose a novel Efficient Scene Text Image
Super-resolution (ESTISR) Network for resource-limited deployment platform.
ESTISR's functionality primarily depends on two critical components: a
CNN-based feature extractor and an efficient self-attention mechanism used for
decoding low-resolution images. We designed a re-parameterized inverted
residual block specifically suited for resource-limited circumstances as the
feature extractor. Meanwhile, we proposed a novel self-attention mechanism,
softmax shrinking, based on a kernel-based approach. This innovative technique
offers linear complexity while also naturally incorporating discriminating
low-level features into the self-attention structure. Extensive experiments on
TextZoom show that ESTISR retains a high image restoration quality and improved
STR accuracy of low-resolution images. Furthermore, ESTISR consistently
outperforms current methods in terms of actual running time and peak memory
consumption, while achieving a better trade-off between performance and
efficiency.
