Abstract
Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of highquality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs. Code is available at this https URL
Abstract (translated)
场景文本图像超分辨率显著提高了场景文本识别的准确性。然而，许多现有方法强调性能而非效率，并忽略了在部署场景中实现轻量级解决方案的实际需求。面对这些问题，我们的工作提出了一种高效的框架SGENet，以促进在资源受限平台上部署。SGENet包含两个分支：超分辨率分支和语义引导分支。我们使用轻量预训练识别器作为语义提取器来增强文本信息的理解。同时，我们设计了一个视觉语义对齐模块，以实现图像特征和语义之间的双向对齐，从而生成高质量的先验指导。我们在基准数据集上进行广泛的实验，与提出的SGENet相比，具有卓越的性能，但计算成本较低。代码可在此处下载：https://url
URL
https://arxiv.org/abs/2403.13330
